The microRNA Mystery Evolution Struggles to Explain

And this is what you hear on the And this is what you hear on the internet. These these biologists will internet. These these biologists will internet. These these biologists will come on and they'll say things and come on and they'll say things and come on and they'll say things and they'll say a just so story with no they'll say a just so story with no they'll say a just so story with no appreciation for what happens with their appreciation for what happens with their appreciation for what happens with their gesso story. And you try to engage these gesso story. And you try to engage these gesso story. And you try to engage these these biologists and they won't engage these biologists and they won't engage these biologists and they won't engage because they like their gesso stories. because they like their gesso stories. because they like their gesso stories. They don't want to try to think of the They don't want to try to think of the They don't want to try to think of the downstream details of those things going downstream details of those things going downstream details of those things going on. We're going to be inviting on. We're going to be inviting on. We're going to be inviting biologists to come and to talk with us. biologists to come and to talk with us. biologists to come and to talk with us. Origin of life people. We're going to Origin of life people. We're going to Origin of life people. We're going to invite them and say, "Okay, tell us tell invite them and say, "Okay, tell us tell invite them and say, "Okay, tell us tell us how life started. >> Hi, we're here again with the >> Hi, we're here again with the conversations group and uh we're going conversations group and uh we're going conversations group and uh we're going to hear a presentation today from from to hear a presentation today from from to hear a presentation today from from Royal and he's going to talk about Royal and he's going to talk about Royal and he's going to talk about cellular computing using biological cellular computing using biological cellular computing using biological codes. He's recently published a paper codes. He's recently published a paper codes. He's recently published a paper on this which we'll cite down in the on this which we'll cite down in the on this which we'll cite down in the description box and he's also written a description box and he's also written a description box and he's also written a lay article on this and uh uh we'll cite lay article on this and uh uh we'll cite lay article on this and uh uh we'll cite that as well. And so we'll see just uh that as well. And so we'll see just uh that as well. And so we'll see just uh uh Royal's thoughts on on how biological uh Royal's thoughts on on how biological uh Royal's thoughts on on how biological systems are are are really uh uh how do systems are are are really uh uh how do systems are are are really uh uh how do they store their code? How do they use they store their code? How do they use they store their code? How do they use their code? It's really an extraordinary their code? It's really an extraordinary their code? It's really an extraordinary thing and hopefully you'll you'll you'll thing and hopefully you'll you'll you'll thing and hopefully you'll you'll you'll enjoy this and then we'll we'll have a enjoy this and then we'll we'll have a enjoy this and then we'll we'll have a conversation about it. So I turn it over conversation about it. So I turn it over conversation about it. So I turn it over to you Royal. Okay, today I'm going to to you Royal. Okay, today I'm going to to you Royal. Okay, today I'm going to talk about cellular uh computing using talk about cellular uh computing using talk about cellular uh computing using biological codes. Here is a quick biological codes. Here is a quick biological codes. Here is a quick overview. We'll be viewing cells today overview. We'll be viewing cells today overview. We'll be viewing cells today as information processing devices.

as information processing devices. as information processing devices. Three key points will be that I believe Three key points will be that I believe Three key points will be that I believe biological codes regulate most cellular biological codes regulate most cellular biological codes regulate most cellular programs. I'm tempted to say all, but programs. I'm tempted to say all, but programs. I'm tempted to say all, but let's just say most. I'm going to let's just say most. I'm going to let's just say most. I'm going to mention some programming details mention some programming details mention some programming details and note the analog nature of these and note the analog nature of these and note the analog nature of these cellular programs. All this raises then cellular programs. All this raises then cellular programs. All this raises then the obvious question, the obvious question, the obvious question, do complex cellular programs arise do complex cellular programs arise do complex cellular programs arise naturalistically? naturalistically? naturalistically? So I'm going to look today at only one So I'm going to look today at only one So I'm going to look today at only one code. I'm going to consider a code. I'm going to consider a code. I'm going to consider a theoretical naturalist origin of the MIA theoretical naturalist origin of the MIA theoretical naturalist origin of the MIA code a technology an issue is that the code a technology an issue is that the code a technology an issue is that the original organisms the individuals that original organisms the individuals that original organisms the individuals that are supposed to have initiated the are supposed to have initiated the are supposed to have initiated the process of creating this new code would process of creating this new code would process of creating this new code would have been disadvantaged over time. And have been disadvantaged over time. And have been disadvantaged over time. And then I'm going use some standard then I'm going use some standard then I'm going use some standard population genetic concepts to show that population genetic concepts to show that population genetic concepts to show that this contradicts the notion of an this contradicts the notion of an this contradicts the notion of an evolutionary origin. So life is more evolutionary origin. So life is more evolutionary origin. So life is more than chemicals in a container. Organisms than chemicals in a container. Organisms than chemicals in a container. Organisms have the property of being holistic.

have the property of being holistic. have the property of being holistic. Everything seems to work as an entity. Everything seems to work as an entity. Everything seems to work as an entity. Even though there are many complex Even though there are many complex Even though there are many complex subsystems involved, all the different subsystems involved, all the different subsystems involved, all the different parts have to be in the correct location parts have to be in the correct location parts have to be in the correct location in the right concentration. The timing in the right concentration. The timing in the right concentration. The timing of assembly has to be correct. Mistakes of assembly has to be correct. Mistakes of assembly has to be correct. Mistakes have to be avoided in how the individual have to be avoided in how the individual have to be avoided in how the individual parts are put are assembled and the parts are put are assembled and the parts are put are assembled and the correct proportion not just the the correct proportion not just the the correct proportion not just the the absolute number but the proportion of absolute number but the proportion of absolute number but the proportion of components components components uh must be correct. you know got five uh must be correct. you know got five uh must be correct. you know got five fingers and you know not at four and a fingers and you know not at four and a fingers and you know not at four and a half and there are many mechanisms to half and there are many mechanisms to half and there are many mechanisms to avoid errors. So some observations avoid errors. So some observations avoid errors. So some observations offspring of organisms increase in offspring of organisms increase in offspring of organisms increase in complexity during their lifetime during complexity during their lifetime during complexity during their lifetime during development and later life. That means development and later life. That means development and later life. That means that during the lifetime different that during the lifetime different that during the lifetime different components are added, repaired, recycled components are added, repaired, recycled components are added, repaired, recycled countless times and this is done countless times and this is done countless times and this is done repeatedly and very very reliably. So repeatedly and very very reliably. So repeatedly and very very reliably. So we're talking about here is a stunning we're talking about here is a stunning we're talking about here is a stunning technology.

technology. technology. Now two key observations. Biological Now two key observations. Biological Now two key observations. Biological codes regulate what to do, where, when, codes regulate what to do, where, when, codes regulate what to do, where, when, how often. And the other one is that how often. And the other one is that how often. And the other one is that more than 99% of biochemical reactions more than 99% of biochemical reactions more than 99% of biochemical reactions require a protein enzyme. So there require a protein enzyme. So there require a protein enzyme. So there thousands of them. How do these two thousands of them. How do these two thousands of them. How do these two ideas tie together? The key here is that ideas tie together? The key here is that ideas tie together? The key here is that enzymes are typically part of the enzymes are typically part of the enzymes are typically part of the hardware, the infrastructure hardware, the infrastructure hardware, the infrastructure used to create these codes. The codes used to create these codes. The codes used to create these codes. The codes themselves already include an enzyme. themselves already include an enzyme. themselves already include an enzyme. And when this is not the case, the And when this is not the case, the And when this is not the case, the hardware of these codes is intimately hardware of these codes is intimately hardware of these codes is intimately and directly linked to an enzyme. The and directly linked to an enzyme. The and directly linked to an enzyme. The consequence therefore is that the consequence therefore is that the consequence therefore is that the molecules in a cell are being processed molecules in a cell are being processed molecules in a cell are being processed one after the other. It's a very very one after the other. It's a very very one after the other. It's a very very precise level of control. This is not a precise level of control. This is not a precise level of control. This is not a bunch of chemicals thrown together in a bunch of chemicals thrown together in a bunch of chemicals thrown together in a container. Each one reacting container. Each one reacting container. Each one reacting independently. It's extremely highly independently. It's extremely highly independently. It's extremely highly regulated. Reminds a little bit about regulated. Reminds a little bit about regulated. Reminds a little bit about what the Bible says. And God saw what the Bible says. And God saw what the Bible says. And God saw everything he had made and behold it was everything he had made and behold it was everything he had made and behold it was very good. And I can surely very good. And I can surely very good. And I can surely attest to that as a scientist.

attest to that as a scientist. attest to that as a scientist. And in Psalms we read, I praise you for And in Psalms we read, I praise you for And in Psalms we read, I praise you for I am fearfully and wonderfully made. I am fearfully and wonderfully made. I am fearfully and wonderfully made. Wonderful are your works. The more we Wonderful are your works. The more we Wonderful are your works. The more we learn about science, the more we can learn about science, the more we can learn about science, the more we can confirm this. It is remarkable that confirm this. It is remarkable that confirm this. It is remarkable that humans use codes or language symbols humans use codes or language symbols humans use codes or language symbols naturally. naturally. naturally. We speak thousands of different We speak thousands of different We speak thousands of different languages and we invent communication languages and we invent communication languages and we invent communication systems effortlessly whether they're the systems effortlessly whether they're the systems effortlessly whether they're the Indian, you know, smoke signals or Indian, you know, smoke signals or Indian, you know, smoke signals or African drums, crystling codes in the African drums, crystling codes in the African drums, crystling codes in the Amazon Indians. We we do this all the Amazon Indians. We we do this all the Amazon Indians. We we do this all the time. Reminds me of a famous passage in time. Reminds me of a famous passage in time. Reminds me of a famous passage in John 1. In the beginning was the word John 1. In the beginning was the word John 1. In the beginning was the word and the word was with God and the word and the word was with God and the word and the word was with God and the word was God. He was in the beginning with was God. He was in the beginning with was God. He was in the beginning with God. All things were made by him. Now in God. All things were made by him. Now in God. All things were made by him. Now in the Bible, we we very often encounter the Bible, we we very often encounter the Bible, we we very often encounter that God expresses that God expresses that God expresses his thoughts, his intentions and about his thoughts, his intentions and about his thoughts, his intentions and about himself using language. And here we know himself using language. And here we know himself using language. And here we know that this passage is referring to Jesus.

that this passage is referring to Jesus. that this passage is referring to Jesus. And this is a rather uh interesting And this is a rather uh interesting And this is a rather uh interesting idea. Jesus is expressing idea. Jesus is expressing idea. Jesus is expressing what God is like. his character's nature what God is like. his character's nature what God is like. his character's nature and in a manner that we humans can and in a manner that we humans can and in a manner that we humans can understand. understand. understand. So we see here a very deep link between So we see here a very deep link between So we see here a very deep link between God, humans and language. In Genesis we God, humans and language. In Genesis we God, humans and language. In Genesis we read that God said let us make man in read that God said let us make man in read that God said let us make man in our image after our likeness. So I our image after our likeness. So I our image after our likeness. So I believe that one of the characteristics, believe that one of the characteristics, believe that one of the characteristics, divine characteristics of God, language divine characteristics of God, language divine characteristics of God, language and being able to express let's say our and being able to express let's say our and being able to express let's say our thoughts and wishes very very precisely thoughts and wishes very very precisely thoughts and wishes very very precisely is something that God has endowed us is something that God has endowed us is something that God has endowed us with. Why am I saying all this? Okay. with. Why am I saying all this? Okay. with. Why am I saying all this? Okay. Um, I I just want to make the point that Um, I I just want to make the point that Um, I I just want to make the point that humans are uniquely qualified to humans are uniquely qualified to humans are uniquely qualified to recognize symbolic languages and recognize symbolic languages and recognize symbolic languages and information processing technology information processing technology information processing technology because I believe God gave this ability. because I believe God gave this ability. because I believe God gave this ability. And this therefore means that we are And this therefore means that we are And this therefore means that we are uniquely qualified to take a deep look uniquely qualified to take a deep look uniquely qualified to take a deep look into how God put together cells and to into how God put together cells and to into how God put together cells and to recognize the information processing and recognize the information processing and recognize the information processing and the languages involved. Now all codes the languages involved. Now all codes the languages involved. Now all codes consist of two parts. One is they have a consist of two parts. One is they have a consist of two parts. One is they have a recognition element. Think of this as recognition element. Think of this as recognition element. Think of this as like a sensor and then there are like a sensor and then there are like a sensor and then there are specificity factors that recognize and specificity factors that recognize and specificity factors that recognize and interact with these sensors. So in the

interact with these sensors. So in the interact with these sensors. So in the case of cells case of cells case of cells uh the example that I'm illustrating uh the example that I'm illustrating uh the example that I'm illustrating here would be the receptors on the here would be the receptors on the here would be the receptors on the outside and these receptors these outside and these receptors these outside and these receptors these recognition elements can recognize recognition elements can recognize recognition elements can recognize hormones and neurotransmitters and hormones and neurotransmitters and hormones and neurotransmitters and ferommones and odorants and ions and all ferommones and odorants and ions and all ferommones and odorants and ions and all kinds of stuff. This is a very kinds of stuff. This is a very kinds of stuff. This is a very fundamental concept. Another fundamental concept. Another fundamental concept. Another illustration here. You've got DNA and illustration here. You've got DNA and illustration here. You've got DNA and certain patterns of nucleotides jointly certain patterns of nucleotides jointly certain patterns of nucleotides jointly define a geometry. Sometimes it is a define a geometry. Sometimes it is a define a geometry. Sometimes it is a shape, sometimes it is a physical shape, sometimes it is a physical shape, sometimes it is a physical property like like charges and things. property like like charges and things. property like like charges and things. The point is those act as recognition The point is those act as recognition The point is those act as recognition elements to which in this case proteins elements to which in this case proteins elements to which in this case proteins called transcription factors can bind. called transcription factors can bind. called transcription factors can bind. So here we see the two elements again a So here we see the two elements again a So here we see the two elements again a recognition element think of it as a recognition element think of it as a recognition element think of it as a sensor and then uh certain uh signals or sensor and then uh certain uh signals or sensor and then uh certain uh signals or specificity factors that can attach specificity factors that can attach specificity factors that can attach there. So this is the one challenging there. So this is the one challenging there. So this is the one challenging slide in the whole slide deck. Um if slide in the whole slide deck. Um if slide in the whole slide deck. Um if this doesn't make a lot of sense don't this doesn't make a lot of sense don't this doesn't make a lot of sense don't worry about it. it doesn't really worry about it. it doesn't really worry about it. it doesn't really prevent you from understanding the rest prevent you from understanding the rest prevent you from understanding the rest of the presentation, but I'm hoping of the presentation, but I'm hoping of the presentation, but I'm hoping there will be people that understand there will be people that understand there will be people that understand enough about molecular biology and enough about molecular biology and enough about molecular biology and computer science or programming so that

computer science or programming so that computer science or programming so that this makes some sense because this then this makes some sense because this then this makes some sense because this then opens the door to a very deep opens the door to a very deep opens the door to a very deep understanding and appreciation for how understanding and appreciation for how understanding and appreciation for how cells work. and also uh an an an area of cells work. and also uh an an an area of cells work. and also uh an an an area of research that I am I'm deeply involved research that I am I'm deeply involved research that I am I'm deeply involved in. First we have the notion of a in. First we have the notion of a in. First we have the notion of a variable and value and values. This is variable and value and values. This is variable and value and values. This is used in all programming language but use used in all programming language but use used in all programming language but use also mathematics. So variables are also mathematics. So variables are also mathematics. So variables are assigned values like in computer assigned values like in computer assigned values like in computer programming we might say price is $100 programming we might say price is $100 programming we might say price is $100 or price is $120. The variable is price or price is $120. The variable is price or price is $120. The variable is price and then the value is what it's assigned and then the value is what it's assigned and then the value is what it's assigned to. to. to. So in sales the recognition element is So in sales the recognition element is So in sales the recognition element is playing the physical playing the physical playing the physical aspect of a variable for example binding aspect of a variable for example binding aspect of a variable for example binding site and the values are what then uh site and the values are what then uh site and the values are what then uh bind or attach with it for example bind or attach with it for example bind or attach with it for example transcription factors. Hope that's not transcription factors. Hope that's not transcription factors. Hope that's not too abstract. So here we have DNA and in too abstract. So here we have DNA and in too abstract. So here we have DNA and in the promoter region there's often in the promoter region there's often in the promoter region there's often in some organism so-called EBOX and it is some organism so-called EBOX and it is some organism so-called EBOX and it is simply these six nucleotides that simply these six nucleotides that simply these six nucleotides that together create a three-dimensional together create a three-dimensional together create a three-dimensional recognition pattern that can recognition pattern that can recognition pattern that can successfully interact with some proteins successfully interact with some proteins successfully interact with some proteins if and only if they have a complimentary

if and only if they have a complimentary if and only if they have a complimentary part here. is complimentary by the way part here. is complimentary by the way part here. is complimentary by the way using amino acids not nucleotides that using amino acids not nucleotides that using amino acids not nucleotides that is able to recognize this particular is able to recognize this particular is able to recognize this particular pattern. So for the EBOX there are six pattern. So for the EBOX there are six pattern. So for the EBOX there are six transcription factors that are endowed transcription factors that are endowed transcription factors that are endowed with a little portion little domain here with a little portion little domain here with a little portion little domain here that allows them to bind here. So that allows them to bind here. So that allows them to bind here. So depending on which one is bound it has a depending on which one is bound it has a depending on which one is bound it has a different outcome. So this is what I different outcome. So this is what I different outcome. So this is what I mean about different values. If this is mean about different values. If this is mean about different values. If this is bound here then this variable has that bound here then this variable has that bound here then this variable has that value. One value of course could be value. One value of course could be value. One value of course could be nothing is bound at all. That is the nothing is bound at all. That is the nothing is bound at all. That is the idea. Here we have also in computer idea. Here we have also in computer idea. Here we have also in computer science the idea of valid data types. science the idea of valid data types. science the idea of valid data types. For example, one could say a date. Uh For example, one could say a date. Uh For example, one could say a date. Uh when is something due? Something is due when is something due? Something is due when is something due? Something is due the value can only be a date like um the value can only be a date like um the value can only be a date like um December 3rd 2008 whatever. You can't December 3rd 2008 whatever. You can't December 3rd 2008 whatever. You can't say day dute is blue or you know day say day dute is blue or you know day say day dute is blue or you know day date blue is three 3.14 because that date blue is three 3.14 because that date blue is three 3.14 because that would be the wrong data type. And this would be the wrong data type. And this would be the wrong data type. And this is valuable because it allows a lot of is valuable because it allows a lot of is valuable because it allows a lot of obvious errors to be corrected. And this obvious errors to be corrected. And this obvious errors to be corrected. And this is uh very very important and uh very is uh very very important and uh very is uh very very important and uh very fundamental to the way the cell fundamental to the way the cell fundamental to the way the cell programming languages uh work. For programming languages uh work. For programming languages uh work. For example, a codon can only bind to example, a codon can only bind to example, a codon can only bind to certain anti-codons. This is super certain anti-codons. This is super certain anti-codons. This is super important because a codon remember is important because a codon remember is important because a codon remember is made up of three triplets and that a made up of three triplets and that a made up of three triplets and that a particular triplet pattern could be

particular triplet pattern could be particular triplet pattern could be present in other RNAs that are not present in other RNAs that are not present in other RNAs that are not messenger RNAs are not being translated messenger RNAs are not being translated messenger RNAs are not being translated and you really don't want the and you really don't want the and you really don't want the anti-codons the tRNA binding there. So anti-codons the tRNA binding there. So anti-codons the tRNA binding there. So that means that um it's not just the that means that um it's not just the that means that um it's not just the code on itself but it's a data type and code on itself but it's a data type and code on itself but it's a data type and it's got certain structural features it's got certain structural features it's got certain structural features around it that make it unique and around it that make it unique and around it that make it unique and distinct. So it's a data type. So cy distinct. So it's a data type. So cy distinct. So it's a data type. So cy elements parts of DNA elements parts of DNA elements parts of DNA that that that are allowed to only bind certain sets of are allowed to only bind certain sets of are allowed to only bind certain sets of transcription factors would be a data transcription factors would be a data transcription factors would be a data type. So in this case that we gave here type. So in this case that we gave here type. So in this case that we gave here uh this data type here EBOX can only be uh this data type here EBOX can only be uh this data type here EBOX can only be assigned a subset of all proteins assigned a subset of all proteins assigned a subset of all proteins uh defined here. uh defined here. uh defined here. >> If you do not believe in the physical >> If you do not believe in the physical >> If you do not believe in the physical resurrection of Jesus Christ send me an resurrection of Jesus Christ send me an resurrection of Jesus Christ send me an email tour.org and we will get together and I will and we will get together and I will share with you about why I embrace the share with you about why I embrace the share with you about why I embrace the resurrection of Jesus. Few more things resurrection of Jesus. Few more things resurrection of Jesus. Few more things we use in programming all the time data we use in programming all the time data we use in programming all the time data structures. It's just a way to organize structures. It's just a way to organize structures. It's just a way to organize data in a manner that optimizes how it data in a manner that optimizes how it data in a manner that optimizes how it can be processed. So one example are can be processed. So one example are can be processed. So one example are arrays which are just uh contiguous arrays which are just uh contiguous arrays which are just uh contiguous um sequences of elements. We see this

um sequences of elements. We see this um sequences of elements. We see this for example in messenger RNAs that uh for example in messenger RNAs that uh for example in messenger RNAs that uh there are a contiguous set of them. Go there are a contiguous set of them. Go there are a contiguous set of them. Go back one. Another element um are records back one. Another element um are records back one. Another element um are records uh records. Examples would be the uh records. Examples would be the uh records. Examples would be the primary transcript of an RNA that is a primary transcript of an RNA that is a primary transcript of an RNA that is a record or chromosomes. The idea is a record or chromosomes. The idea is a record or chromosomes. The idea is a record consists of subp parts like record consists of subp parts like record consists of subp parts like fields or as anie pointed out a couple fields or as anie pointed out a couple fields or as anie pointed out a couple days ago records is a term used also in days ago records is a term used also in days ago records is a term used also in databases. databases. databases. So if you have different items that are So if you have different items that are So if you have different items that are being extracted from a database all of being extracted from a database all of being extracted from a database all of this these collection of items together this these collection of items together this these collection of items together can be assembled and processed as can be assembled and processed as can be assembled and processed as processed as one unit as records. So the processed as one unit as records. So the processed as one unit as records. So the it makes sense to do this because it makes sense to do this because it makes sense to do this because depending on your purposes you can you depending on your purposes you can you depending on your purposes you can you can process these data structures in can process these data structures in can process these data structures in different ways. You could process a different ways. You could process a different ways. You could process a record as a total entity if you just record as a total entity if you just record as a total entity if you just want to duplicate it without any want to duplicate it without any want to duplicate it without any interest in the internal content and interest in the internal content and interest in the internal content and vice versa. You may be interested vice versa. You may be interested vice versa. You may be interested internal content. The internal content internal content. The internal content internal content. The internal content here for example could be coding regions here for example could be coding regions here for example could be coding regions or polyatails and and other things. So or polyatails and and other things. So or polyatails and and other things. So this is just simply a way to organize uh this is just simply a way to organize uh this is just simply a way to organize uh data and in programming and also in data and in programming and also in data and in programming and also in cellular programming one combines and cellular programming one combines and cellular programming one combines and data types and data structures and this data types and data structures and this data types and data structures and this is a way to run then uh programs the

is a way to run then uh programs the is a way to run then uh programs the actual logic is performed on data types actual logic is performed on data types actual logic is performed on data types and data structures. Um, those of you and data structures. Um, those of you and data structures. Um, those of you who have programmed may recognize things who have programmed may recognize things who have programmed may recognize things like iterations or if you're as old as I like iterations or if you're as old as I like iterations or if you're as old as I am, dual loops from forran and things am, dual loops from forran and things am, dual loops from forran and things like that. Uh, those are the kinds of like that. Uh, those are the kinds of like that. Uh, those are the kinds of operations that be carried out. In the operations that be carried out. In the operations that be carried out. In the case of cells, there are many many kinds case of cells, there are many many kinds case of cells, there are many many kinds of operations going on. When you convert of operations going on. When you convert of operations going on. When you convert part of a DNA, a DNA gene, it is part of a DNA, a DNA gene, it is part of a DNA, a DNA gene, it is processing processing processing some of the DNA sets of nucleotides, some of the DNA sets of nucleotides, some of the DNA sets of nucleotides, therefore called an array, therefore called an array, therefore called an array, and can create a similar molecule, an and can create a similar molecule, an and can create a similar molecule, an RNA. And this has the property what I RNA. And this has the property what I RNA. And this has the property what I would call a Q because Q's are defined would call a Q because Q's are defined would call a Q because Q's are defined as a series of elements where you're as a series of elements where you're as a series of elements where you're always adding things at at the top. So always adding things at at the top. So always adding things at at the top. So you're only adding it to one end which you're only adding it to one end which you're only adding it to one end which happens to be what's what happens when happens to be what's what happens when happens to be what's what happens when you transcribe you transcribe you transcribe or when you translate messenger RNAs or when you translate messenger RNAs or when you translate messenger RNAs you're you're translating a a set of you're you're translating a a set of you're you're translating a a set of three at a time. Three three three.

three at a time. Three three three. three at a time. Three three three. So that's an array. In this case, the So that's an array. In this case, the So that's an array. In this case, the array is made up of three elements here array is made up of three elements here array is made up of three elements here actually of only one, the nucleotide. actually of only one, the nucleotide. actually of only one, the nucleotide. But these three But these three But these three elements taken as a elements taken as a elements taken as a joint unit create a protein. So the joint unit create a protein. So the joint unit create a protein. So the protein what you're doing is you're protein what you're doing is you're protein what you're doing is you're adding amino acid one after the other adding amino acid one after the other adding amino acid one after the other based on the on the codon sequence and based on the on the codon sequence and based on the on the codon sequence and you're adding it at one end. So this is you're adding it at one end. So this is you're adding it at one end. So this is a classical example of a Q data type. a classical example of a Q data type. a classical example of a Q data type. Ancy pointed out that uh this is very Ancy pointed out that uh this is very Ancy pointed out that uh this is very similar to what some may call a stack. similar to what some may call a stack. similar to what some may call a stack. There it's a very similar concept. It There it's a very similar concept. It There it's a very similar concept. It all depends on whether you're adding all depends on whether you're adding all depends on whether you're adding things at one end or the other end. It's things at one end or the other end. It's things at one end or the other end. It's a very similar concept and the various a very similar concept and the various a very similar concept and the various coding regions can overlap. This we do coding regions can overlap. This we do coding regions can overlap. This we do not do in programming. It's just simply not do in programming. It's just simply not do in programming. It's just simply too difficult. The idea is uh let's use too difficult. The idea is uh let's use too difficult. The idea is uh let's use DNA that the same long region of DNA can DNA that the same long region of DNA can DNA that the same long region of DNA can be used for many purposes which is be used for many purposes which is be used for many purposes which is really astonishing.

really astonishing. really astonishing. One region could be used to specify a One region could be used to specify a One region could be used to specify a sequence of proteins. In other words, sequence of proteins. In other words, sequence of proteins. In other words, the instructions part of the same one the instructions part of the same one the instructions part of the same one could be used for regulatory purposes could be used for regulatory purposes could be used for regulatory purposes like how fast, when to start, when to like how fast, when to start, when to like how fast, when to start, when to stop. product can be used for epigenetic stop. product can be used for epigenetic stop. product can be used for epigenetic purposes etc. So it's a very very clever purposes etc. So it's a very very clever purposes etc. So it's a very very clever and very very compact way of programming and very very compact way of programming and very very compact way of programming and incidentally one way one reason also and incidentally one way one reason also and incidentally one way one reason also for for for uh certain redundancies for example that uh certain redundancies for example that uh certain redundancies for example that um different codons can all code for the um different codons can all code for the um different codons can all code for the same protein because you may need to same protein because you may need to same protein because you may need to select a different code on for that select a different code on for that select a different code on for that protein in order not to mess up another protein in order not to mess up another protein in order not to mess up another code. And then there's a grammar just code. And then there's a grammar just code. And then there's a grammar just like in in human languages to avoid like in in human languages to avoid like in in human languages to avoid mistakes and ambiguity. All programming mistakes and ambiguity. All programming mistakes and ambiguity. All programming languages have a certain set of rules. languages have a certain set of rules. languages have a certain set of rules. This is very very apparent in cells. How This is very very apparent in cells. How This is very very apparent in cells. How when to to transcribe genes for example when to to transcribe genes for example when to to transcribe genes for example there's a very clear logic. If this then there's a very clear logic. If this then there's a very clear logic. If this then that but if this and don't do it and that but if this and don't do it and that but if this and don't do it and things like that. How and when to things like that. How and when to things like that. How and when to transllocate proteins different regions.

transllocate proteins different regions. transllocate proteins different regions. These are all grammarss. These are all grammarss. These are all grammarss. This is I know difficult. I've talked This is I know difficult. I've talked This is I know difficult. I've talked about these things to many people over about these things to many people over about these things to many people over the years. Typically the molecular the years. Typically the molecular the years. Typically the molecular biologist say they understand everything biologist say they understand everything biologist say they understand everything I say when I'm talking about molecular I say when I'm talking about molecular I say when I'm talking about molecular biology but they're lost when I talk biology but they're lost when I talk biology but they're lost when I talk about the computer part and vice versa. about the computer part and vice versa. about the computer part and vice versa. Right? It's not critically important. Right? It's not critically important. Right? It's not critically important. It's not really necessary to understand It's not really necessary to understand It's not really necessary to understand all of this uh for my talk. But I'm all of this uh for my talk. But I'm all of this uh for my talk. But I'm hoping that will give an appreciation hoping that will give an appreciation hoping that will give an appreciation that we are really talking about a that we are really talking about a that we are really talking about a computing technology. For those who computing technology. For those who computing technology. For those who spent the time looking into this, it spent the time looking into this, it spent the time looking into this, it it's it's obvious. It just it's hard to it's it's obvious. It just it's hard to it's it's obvious. It just it's hard to a avoid. But before I move on with the a avoid. But before I move on with the a avoid. But before I move on with the main part of my talk, does anybody have main part of my talk, does anybody have main part of my talk, does anybody have any ideas, suggestions that can make any ideas, suggestions that can make any ideas, suggestions that can make this more tangible or this more tangible or this more tangible or some comments they would like to make? some comments they would like to make? some comments they would like to make? >> Yeah, really. I think sometimes this >> Yeah, really. I think sometimes this >> Yeah, really. I think sometimes this line of reasoning gets critiqued for line of reasoning gets critiqued for line of reasoning gets critiqued for being analogical, right? You're making being analogical, right? You're making being analogical, right? You're making an analogy, right? It looks like code.

an analogy, right? It looks like code. an analogy, right? It looks like code. Um it uh you know there's it it looks Um it uh you know there's it it looks Um it uh you know there's it it looks like there are data structures but um it like there are data structures but um it like there are data structures but um it isn't real. It's not actually a code. Uh isn't real. It's not actually a code. Uh isn't real. It's not actually a code. Uh I wonder if you have any thoughts around I wonder if you have any thoughts around I wonder if you have any thoughts around that kind of push back. Well, reminds that kind of push back. Well, reminds that kind of push back. Well, reminds you a little bit about the principle of you a little bit about the principle of you a little bit about the principle of math. When somebody says, "We have two math. When somebody says, "We have two math. When somebody says, "We have two functions, but for every single value, functions, but for every single value, functions, but for every single value, you always get the exact same result. you always get the exact same result. you always get the exact same result. There's absolutely no way to distinguish There's absolutely no way to distinguish There's absolutely no way to distinguish two functions." That a mathematician two functions." That a mathematician two functions." That a mathematician would say they are the same function. would say they are the same function. would say they are the same function. Um, I have heard those comments that is Um, I have heard those comments that is Um, I have heard those comments that is correct. correct. correct. But it's impossible for me and I haven't But it's impossible for me and I haven't But it's impossible for me and I haven't found anybody else to say why is only an found anybody else to say why is only an found anybody else to say why is only an analogy and not true. If somebody says analogy and not true. If somebody says analogy and not true. If somebody says four turns a programming language but four turns a programming language but four turns a programming language but Pascal is only an analogy I would say Pascal is only an analogy I would say Pascal is only an analogy I would say well tell me a little bit about why you well tell me a little bit about why you well tell me a little bit about why you think think think one is one is not. And it um I think in one is one is not. And it um I think in one is one is not. And it um I think in programming you could look at the source programming you could look at the source programming you could look at the source code, you understand it, you make some code, you understand it, you make some code, you understand it, you make some changes and indeed you get the result changes and indeed you get the result changes and indeed you get the result you expected from stand the logic. the you expected from stand the logic. the you expected from stand the logic. the cells the same way. When you when you cells the same way. When you when you cells the same way. When you when you understand the programming logic, it understand the programming logic, it understand the programming logic, it allows you to make precise predictions.

allows you to make precise predictions. allows you to make precise predictions. And at at that point, these predictions And at at that point, these predictions And at at that point, these predictions are not based on any kind of chemical are not based on any kind of chemical are not based on any kind of chemical principles, rotation, vibration, principles, rotation, vibration, principles, rotation, vibration, chemical bonds, things like that. But chemical bonds, things like that. But chemical bonds, things like that. But upon but the logic is real logic. It's upon but the logic is real logic. It's upon but the logic is real logic. It's something that's independent of the something that's independent of the something that's independent of the implementation. And when you can make implementation. And when you can make implementation. And when you can make pre when you can make precise pre when you can make precise pre when you can make precise predictions predictions predictions uh at that point it goes beyond an uh at that point it goes beyond an uh at that point it goes beyond an analogy I believe it's real. analogy I believe it's real. analogy I believe it's real. >> I think analogies like this have have a >> I think analogies like this have have a >> I think analogies like this have have a lot of value helping helping to lot of value helping helping to lot of value helping helping to understand how life works you know understand how life works you know understand how life works you know through an analogy like this. But I through an analogy like this. But I through an analogy like this. But I think also that any kind of analogy I've think also that any kind of analogy I've think also that any kind of analogy I've seen applied to life ends up falling seen applied to life ends up falling seen applied to life ends up falling short. And I think one way you're about short. And I think one way you're about short. And I think one way you're about to show that this this analogy falls to show that this this analogy falls to show that this this analogy falls short is that there's a lot of analog short is that there's a lot of analog short is that there's a lot of analog stuff going on. It's not all digital. stuff going on. It's not all digital. stuff going on. It's not all digital. And so the the cell is more than this, And so the the cell is more than this, And so the the cell is more than this, but it but it is what you show here and but it but it is what you show here and but it but it is what you show here and then some. Um there's two thumbs to then some. Um there's two thumbs to then some. Um there's two thumbs to that. But more than that, um, in a that. But more than that, um, in a that. But more than that, um, in a couple of of papers where I actually go couple of of papers where I actually go couple of of papers where I actually go into a lot of depth into this, um, I've into a lot of depth into this, um, I've into a lot of depth into this, um, I've pointed out that looking at this as a pointed out that looking at this as a pointed out that looking at this as a technology shows that there are a lot of technology shows that there are a lot of technology shows that there are a lot of principles here that do not apply to principles here that do not apply to principles here that do not apply to anything that we know. For example, I anything that we know. For example, I anything that we know. For example, I said when talking about variables and said when talking about variables and said when talking about variables and values, I said price equals 100.

values, I said price equals 100. values, I said price equals 100. In sales, you can do two things. You can In sales, you can do two things. You can In sales, you can do two things. You can say the variable itself is imprecise say the variable itself is imprecise say the variable itself is imprecise on purpose. You could say it's u it's on purpose. You could say it's u it's on purpose. You could say it's u it's about the variable itself, not the about the variable itself, not the about the variable itself, not the value. I can say the variable is value. I can say the variable is value. I can say the variable is fuzzy. It's near whatever the value is fuzzy. It's near whatever the value is fuzzy. It's near whatever the value is you assign to it and the value itself is you assign to it and the value itself is you assign to it and the value itself is near but not exactly that. It would be near but not exactly that. It would be near but not exactly that. It would be like saying the variable price. If I like saying the variable price. If I like saying the variable price. If I write if I write the word price a little write if I write the word price a little write if I write the word price a little bit larger, a little bit smaller or the bit larger, a little bit smaller or the bit larger, a little bit smaller or the letters slightly spaced apart, it has an letters slightly spaced apart, it has an letters slightly spaced apart, it has an influence. We never do this in any kind influence. We never do this in any kind influence. We never do this in any kind of programming. And this is what these of programming. And this is what these of programming. And this is what these things do occur in cells. So there I see things do occur in cells. So there I see things do occur in cells. So there I see this as a far more advanced kind of this as a far more advanced kind of this as a far more advanced kind of technology than what we would do at the technology than what we would do at the technology than what we would do at the moment. And it it allows there for a lot moment. And it it allows there for a lot moment. And it it allows there for a lot of nuances, a lot of precision, but a of nuances, a lot of precision, but a of nuances, a lot of precision, but a lot of robustness so that it doesn't lot of robustness so that it doesn't lot of robustness so that it doesn't fall apart fall apart fall apart when small mistakes happen. If I have a when small mistakes happen. If I have a when small mistakes happen. If I have a variable price and the P is not variable price and the P is not variable price and the P is not recognizable as a P in price, your recognizable as a P in price, your recognizable as a P in price, your variable's ruined in cells. What happens variable's ruined in cells. What happens variable's ruined in cells. What happens is it still works but slightly modified.

is it still works but slightly modified. is it still works but slightly modified. So there are there are differences. So there are there are differences. So there are there are differences. There definitely differences but all the There definitely differences but all the There definitely differences but all the differences point in the direction of a differences point in the direction of a differences point in the direction of a higher level of specification and more higher level of specification and more higher level of specification and more advanced technology. There are other advanced technology. There are other advanced technology. There are other things. One thing I have not talked things. One thing I have not talked things. One thing I have not talked about is there are other things like you about is there are other things like you about is there are other things like you can organize what happens in a cell in can organize what happens in a cell in can organize what happens in a cell in different compartments like organels and different compartments like organels and different compartments like organels and stuff and you can give them different stuff and you can give them different stuff and you can give them different phes, different concentrations of phes, different concentrations of phes, different concentrations of proteins and that then also has a a proteins and that then also has a a proteins and that then also has a a regulatory aspect. There are those regulatory aspect. There are those regulatory aspect. There are those things also which we won't be talking things also which we won't be talking things also which we won't be talking about today. Almost all cellular about today. Almost all cellular about today. Almost all cellular programming has an analog meaning a programming has an analog meaning a programming has an analog meaning a continuous character. The idea here is continuous character. The idea here is continuous character. The idea here is suppose you've got a container of of suppose you've got a container of of suppose you've got a container of of chemicals and the concentration uh chemicals and the concentration uh chemicals and the concentration uh changes and a device that can sense that changes and a device that can sense that changes and a device that can sense that and then show the the result here. and then show the the result here. and then show the the result here. Analog continuous means that there is a Analog continuous means that there is a Analog continuous means that there is a con a constant uh continuous level of con a constant uh continuous level of con a constant uh continuous level of concentration and response. Therefore, concentration and response. Therefore, concentration and response. Therefore, um you can get a real stat type behavior um you can get a real stat type behavior um you can get a real stat type behavior especially if you have multiple copies especially if you have multiple copies especially if you have multiple copies of the things going on at the same time.

of the things going on at the same time. of the things going on at the same time. So therefore, biological processes occur So therefore, biological processes occur So therefore, biological processes occur along a continuum. When the input signal along a continuum. When the input signal along a continuum. When the input signal varies slightly, then there's a response varies slightly, then there's a response varies slightly, then there's a response uh going on. You don't have this in uh going on. You don't have this in uh going on. You don't have this in other forms of programming. If you other forms of programming. If you other forms of programming. If you repeat many times, price equals 10, the repeat many times, price equals 10, the repeat many times, price equals 10, the price is still 10. But in cellular price is still 10. But in cellular price is still 10. But in cellular programming copies do matter. So if you programming copies do matter. So if you programming copies do matter. So if you have many copies of messenger RNA and have many copies of messenger RNA and have many copies of messenger RNA and many copies of transfer RNAs then indeed many copies of transfer RNAs then indeed many copies of transfer RNAs then indeed you do end up with more protein being you do end up with more protein being you do end up with more protein being formed. So these are some of the formed. So these are some of the formed. So these are some of the differences. So we won't talk about the differences. So we won't talk about the differences. So we won't talk about the different uh examples here but we just different uh examples here but we just different uh examples here but we just mentioned the top one. So metabolic mentioned the top one. So metabolic mentioned the top one. So metabolic pathways like glycolysis are continuous. pathways like glycolysis are continuous. pathways like glycolysis are continuous. That means depending of cell needs you That means depending of cell needs you That means depending of cell needs you can have more or you can have less. And can have more or you can have less. And can have more or you can have less. And this works because the code itself this works because the code itself this works because the code itself affects how fast things get started that affects how fast things get started that affects how fast things get started that guide that complex process. So when guide that complex process. So when guide that complex process. So when you've got the transcription factors you've got the transcription factors you've got the transcription factors that are coming in faster or slower and that are coming in faster or slower and that are coming in faster or slower and they will interact faster or slower with they will interact faster or slower with they will interact faster or slower with the recognition element. These two the recognition element. These two the recognition element. These two things together once they're in place things together once they're in place things together once they're in place attract other components like proteins attract other components like proteins attract other components like proteins and they attract other components until and they attract other components until and they attract other components until you build a complete complex here and in you build a complete complex here and in you build a complete complex here and in this particular example this can this particular example this can this particular example this can interact with uh polymerases and they interact with uh polymerases and they interact with uh polymerases and they lead to transcription. So point here is lead to transcription. So point here is lead to transcription. So point here is that the code itself has an analog

that the code itself has an analog that the code itself has an analog character and that therefore controls character and that therefore controls character and that therefore controls what happens downstream. That brings us what happens downstream. That brings us what happens downstream. That brings us then to the question, could this have then to the question, could this have then to the question, could this have arisen naturalistically? arisen naturalistically? arisen naturalistically? Let's look at one example. MicroRNAs Let's look at one example. MicroRNAs Let's look at one example. MicroRNAs suppose you've got a whole bunch of suppose you've got a whole bunch of suppose you've got a whole bunch of messenger RNAs here. And some of these messenger RNAs here. And some of these messenger RNAs here. And some of these for whatever reason should not be for whatever reason should not be for whatever reason should not be translated at a particular point of translated at a particular point of translated at a particular point of time. Those that should be treated like time. Those that should be treated like time. Those that should be treated like an ensemble have to be tagged or an ensemble have to be tagged or an ensemble have to be tagged or identified in some manner. So those identified in some manner. So those identified in some manner. So those tags, those recognition elements here tags, those recognition elements here tags, those recognition elements here I've shown here in different colors. The I've shown here in different colors. The I've shown here in different colors. The trick here now is that you've got the trick here now is that you've got the trick here now is that you've got the counterpart the SP. Um each one of these counterpart the SP. Um each one of these counterpart the SP. Um each one of these microRNAs here can associate with microRNAs here can associate with microRNAs here can associate with precise other ones precise other ones precise other ones if done correctly. if done correctly. if done correctly. This one here can down reggulate the This one here can down reggulate the This one here can down reggulate the other ones if and only if the other ones if and only if the other ones if and only if the complimentary sequences have been placed complimentary sequences have been placed complimentary sequences have been placed on those messenger RNAs. So if this one on those messenger RNAs. So if this one on those messenger RNAs. So if this one is supposed to be downregulated, it is supposed to be downregulated, it is supposed to be downregulated, it can't because it's missing the can't because it's missing the can't because it's missing the complimentary sequence. So there's a complimentary sequence. So there's a complimentary sequence. So there's a binding here. So you got a whole bunch binding here. So you got a whole bunch binding here. So you got a whole bunch of copies of this and it can then bind of copies of this and it can then bind of copies of this and it can then bind to the ones that are supposed to be to the ones that are supposed to be to the ones that are supposed to be downregulated. If this is not supposed downregulated. If this is not supposed downregulated. If this is not supposed to downregulated, you're messed up to downregulated, you're messed up to downregulated, you're messed up because it's got the sequence it should because it's got the sequence it should because it's got the sequence it should not have. Though this is how the code not have. Though this is how the code not have. Though this is how the code works. The code itself defines what works. The code itself defines what works. The code itself defines what should uh associate with what but this should uh associate with what but this should uh associate with what but this is rarely enough in any code. You then

is rarely enough in any code. You then is rarely enough in any code. You then need other proteins that come together need other proteins that come together need other proteins that come together and they collaborate. They bind and hold and they collaborate. They bind and hold and they collaborate. They bind and hold everything in place correctly. So this everything in place correctly. So this everything in place correctly. So this is true also of micronas. This is called is true also of micronas. This is called is true also of micronas. This is called the risk complex. Different proteins and the risk complex. Different proteins and the risk complex. Different proteins and it holds these two elements together. it holds these two elements together. it holds these two elements together. And only when this occurs is it stable And only when this occurs is it stable And only when this occurs is it stable enough to then continue doing what's enough to then continue doing what's enough to then continue doing what's supposed to be doing. A very important supposed to be doing. A very important supposed to be doing. A very important comment is that this kind of comment is that this kind of comment is that this kind of downregulation only makes sense if done downregulation only makes sense if done downregulation only makes sense if done at the right time. So if a micro RNA is at the right time. So if a micro RNA is at the right time. So if a micro RNA is to to to downregulate several messenger RNAs this downregulate several messenger RNAs this downregulate several messenger RNAs this has to occur for example during a has to occur for example during a has to occur for example during a specific point of development or only specific point of development or only specific point of development or only for different cell types otherwise it's for different cell types otherwise it's for different cell types otherwise it's delterious and so I've given a couple delterious and so I've given a couple delterious and so I've given a couple dozen examples here of the specific dozen examples here of the specific dozen examples here of the specific conditions under which the microna has conditions under which the microna has conditions under which the microna has to be regulated to do their down They to be regulated to do their down They to be regulated to do their down They don't regulation otherwise it's all don't regulation otherwise it's all don't regulation otherwise it's all wrong. So this shows an image of cells wrong. So this shows an image of cells wrong. So this shows an image of cells that are far more than a bag of that are far more than a bag of that are far more than a bag of chemicals. Many programs are regulated.

chemicals. Many programs are regulated. chemicals. Many programs are regulated. You can have multiple copies of the You can have multiple copies of the You can have multiple copies of the recognition elements. For example, on recognition elements. For example, on recognition elements. For example, on all the different messenger RNAs and all the different messenger RNAs and all the different messenger RNAs and therefore you have a lot of parallel therefore you have a lot of parallel therefore you have a lot of parallel processing by placing processing by placing processing by placing these in many MMA locations. You have these in many MMA locations. You have these in many MMA locations. You have different molecular machines operating different molecular machines operating different molecular machines operating in parallel. Now think a little bit in parallel. Now think a little bit in parallel. Now think a little bit about whether this could have arisen about whether this could have arisen about whether this could have arisen naturalistically. naturalistically. naturalistically. All of this would have had to have All of this would have had to have All of this would have had to have started at some point in the far past. started at some point in the far past. started at some point in the far past. And that can only happen if the And that can only happen if the And that can only happen if the pioneer genome would have been larger pioneer genome would have been larger pioneer genome would have been larger than the other ones initially otherwise than the other ones initially otherwise than the other ones initially otherwise whole thing couldn't have gotten whole thing couldn't have gotten whole thing couldn't have gotten started. Micronas are absent in bacteria started. Micronas are absent in bacteria started. Micronas are absent in bacteria and procarots entirely. They have very and procarots entirely. They have very and procarots entirely. They have very very compact genomes, no junk, nothing very compact genomes, no junk, nothing very compact genomes, no junk, nothing to play around with. So the microRNAs to play around with. So the microRNAs to play around with. So the microRNAs themselves down here go through a a themselves down here go through a a themselves down here go through a a series of steps before they're usable. series of steps before they're usable. series of steps before they're usable. First, they have to be encoded on DNA.

First, they have to be encoded on DNA. First, they have to be encoded on DNA. And the first step is it forms a very And the first step is it forms a very And the first step is it forms a very large transcript called a primary large transcript called a primary large transcript called a primary um mRNA, a PRI. This is very quite um mRNA, a PRI. This is very quite um mRNA, a PRI. This is very quite large. large. large. Different proteins then uh come in Different proteins then uh come in Different proteins then uh come in associate with it at a at a precise associate with it at a at a precise associate with it at a at a precise position cut it and that forms what's position cut it and that forms what's position cut it and that forms what's called a pre-marina. called a pre-marina. called a pre-marina. It has a specific structure now that is It has a specific structure now that is It has a specific structure now that is capable of attracting certain proteins capable of attracting certain proteins capable of attracting certain proteins which then splice cut this vertically to which then splice cut this vertically to which then splice cut this vertically to form two parts and one of the two is form two parts and one of the two is form two parts and one of the two is then going to associate with the risk then going to associate with the risk then going to associate with the risk complex I showed before and then things complex I showed before and then things complex I showed before and then things move on. So all of this uh has to occur move on. So all of this uh has to occur move on. So all of this uh has to occur and why is that important? Because all and why is that important? Because all and why is that important? Because all these components have to be coded for on these components have to be coded for on these components have to be coded for on DNA. DNA. DNA. This primary mRNA has got to be part of This primary mRNA has got to be part of This primary mRNA has got to be part of the genome. All the proteins convert the genome. All the proteins convert the genome. All the proteins convert from prior to pre from pre to mRNA and from prior to pre from pre to mRNA and from prior to pre from pre to mRNA and then all the the risk proteins all these then all the the risk proteins all these then all the the risk proteins all these things have to be available on the things have to be available on the things have to be available on the genome initially for anything to get genome initially for anything to get genome initially for anything to get started.

started. started. Now of now some of these proteins are Now of now some of these proteins are Now of now some of these proteins are only used for micronas. only used for micronas. only used for micronas. Other proteins are shared. They're used Other proteins are shared. They're used Other proteins are shared. They're used for other purposes. But now if this for other purposes. But now if this for other purposes. But now if this whole process is to get started, whole process is to get started, whole process is to get started, these proteins are being funneled away these proteins are being funneled away these proteins are being funneled away from their original purpose. That means from their original purpose. That means from their original purpose. That means you've got to create more of it. There you've got to create more of it. There you've got to create more of it. There is another reason why the genomes would is another reason why the genomes would is another reason why the genomes would have had to be much larger and that is have had to be much larger and that is have had to be much larger and that is the micronas have to interact with a the micronas have to interact with a the micronas have to interact with a counterpart on the messenger RNAs. This counterpart on the messenger RNAs. This counterpart on the messenger RNAs. This region here. So this counterpart here region here. So this counterpart here region here. So this counterpart here has to come somewhere. So you got to has to come somewhere. So you got to has to come somewhere. So you got to make larger genomes. And this has got to make larger genomes. And this has got to make larger genomes. And this has got to mutate over a huge number of generations mutate over a huge number of generations mutate over a huge number of generations to create the counterpart for each to create the counterpart for each to create the counterpart for each microRNA. So this creates now three microRNA. So this creates now three microRNA. So this creates now three reasons why this would be reasons why this would be reasons why this would be disadvantageous for the pioneer. First disadvantageous for the pioneer. First disadvantageous for the pioneer. First of all, just having a larger DNA means of all, just having a larger DNA means of all, just having a larger DNA means it's got to be replicated. That takes it's got to be replicated. That takes it's got to be replicated. That takes time. adding just one moment, one minute time. adding just one moment, one minute time. adding just one moment, one minute per day to regeneration time over per day to regeneration time over per day to regeneration time over millions of generations would would mean millions of generations would would mean millions of generations would would mean that lineage would quickly fizzle out that lineage would quickly fizzle out that lineage would quickly fizzle out just mathematically. Also, all of this just mathematically. Also, all of this just mathematically. Also, all of this extra DNA plus RNA per proteins uh is extra DNA plus RNA per proteins uh is extra DNA plus RNA per proteins uh is deletterious. It costs energy. It costs deletterious. It costs energy. It costs deletterious. It costs energy. It costs material. Uh it means that it's far more material. Uh it means that it's far more material. Uh it means that it's far more difficult for that lineage to survive difficult for that lineage to survive difficult for that lineage to survive compared to the rest of population. And compared to the rest of population. And compared to the rest of population. And in addition, a larger DNA is more

in addition, a larger DNA is more in addition, a larger DNA is more airprone. airprone. airprone. There's a fourth reason why this would There's a fourth reason why this would There's a fourth reason why this would have been problematic and it's this is have been problematic and it's this is have been problematic and it's this is actually a more serious reason and that actually a more serious reason and that actually a more serious reason and that is originally by chance the microRNAs is originally by chance the microRNAs is originally by chance the microRNAs would have bound all over the place by would have bound all over the place by would have bound all over the place by chance. Okay, there's no reason in the chance. Okay, there's no reason in the chance. Okay, there's no reason in the world why um a beneficial down world why um a beneficial down world why um a beneficial down regulation regulation regulation uh would have had the complimentary uh would have had the complimentary uh would have had the complimentary sequence on this location by sheer good sequence on this location by sheer good sequence on this location by sheer good luck by you know by evolutionary fortune luck by you know by evolutionary fortune luck by you know by evolutionary fortune that that makes no sense. So the problem that that makes no sense. So the problem that that makes no sense. So the problem here is that to get this whole thing here is that to get this whole thing here is that to get this whole thing started by chance, a lot in fact most of started by chance, a lot in fact most of started by chance, a lot in fact most of the genes would have been misregulated. the genes would have been misregulated. the genes would have been misregulated. These are so-called housekeeping genes These are so-called housekeeping genes These are so-called housekeeping genes that are always needed. These are things that are always needed. These are things that are always needed. These are things to form ribosomes, metabolic enzymes and to form ribosomes, metabolic enzymes and to form ribosomes, metabolic enzymes and whatnot. These things are necessary. You whatnot. These things are necessary. You whatnot. These things are necessary. You don't want to misregulate them. don't want to misregulate them. don't want to misregulate them. Unfortunately also these are the Unfortunately also these are the Unfortunately also these are the important genes that form the most important genes that form the most important genes that form the most transcripts. So therefore they would transcripts. So therefore they would transcripts. So therefore they would have monopolized any microRNA being have monopolized any microRNA being have monopolized any microRNA being formed initially by by chance.

formed initially by by chance. formed initially by by chance. >> So Royal to clarify that you're saying >> So Royal to clarify that you're saying >> So Royal to clarify that you're saying that because the the microRNAs, you that because the the microRNAs, you that because the the microRNAs, you know, they have a code that matches, but know, they have a code that matches, but know, they have a code that matches, but it's not it's not so specific that it it's not it's not so specific that it it's not it's not so specific that it would only pick out one mRNA, right? would only pick out one mRNA, right? would only pick out one mRNA, right? They it's it's not that specific of a They it's it's not that specific of a They it's it's not that specific of a code. It's kind of like a password with code. It's kind of like a password with code. It's kind of like a password with four characters in it. It's not hyper four characters in it. It's not hyper four characters in it. It's not hyper specific. specific. specific. >> Close. What I'm saying is that these >> Close. What I'm saying is that these >> Close. What I'm saying is that these roughly seven nucleotides are supposed roughly seven nucleotides are supposed roughly seven nucleotides are supposed to bind to their seven counterparts to bind to their seven counterparts to bind to their seven counterparts here. Initially, that would be totally here. Initially, that would be totally here. Initially, that would be totally random. Uh this sequence here and the random. Uh this sequence here and the random. Uh this sequence here and the sequence here. So, it's going to bind sequence here. So, it's going to bind sequence here. So, it's going to bind wherever it can bind. wherever it can bind. wherever it can bind. And statistically, by sheer bad luck, And statistically, by sheer bad luck, And statistically, by sheer bad luck, it's usually going to bind to the genes it's usually going to bind to the genes it's usually going to bind to the genes that are already present in large that are already present in large that are already present in large amount. amount. amount. And these things should not it should And these things should not it should And these things should not it should not bind in there because it will not bind in there because it will not bind in there because it will downregulate the stuff that you need. downregulate the stuff that you need. downregulate the stuff that you need. >> Right. So it's not super not super >> Right. So it's not super not super >> Right. So it's not super not super specific as it is. So specific as it is. So specific as it is. So >> initially it's not at all initially it's >> initially it's not at all initially it's >> initially it's not at all initially it's all wrong.

all wrong. all wrong. >> Yeah. So it could cause more trouble >> Yeah. So it could cause more trouble >> Yeah. So it could cause more trouble than than than >> all wrong. Exactly. Because of you, millions of people have Because of you, millions of people have heard the gospel. If you confess with heard the gospel. If you confess with heard the gospel. If you confess with your mouth, Jesus is Lord and believe in your mouth, Jesus is Lord and believe in your mouth, Jesus is Lord and believe in your heart that he's risen from the dead your heart that he's risen from the dead your heart that he's risen from the dead and you will be saved. That's right. and you will be saved. That's right. and you will be saved. That's right. That's the requirement. We talk about That's the requirement. We talk about That's the requirement. We talk about science concepts which draw people in. science concepts which draw people in. science concepts which draw people in. Take these nanom machines and have them Take these nanom machines and have them Take these nanom machines and have them drill into cells. It' be a great way to drill into cells. It' be a great way to drill into cells. It' be a great way to kill cancer, right? We also talk about kill cancer, right? We also talk about kill cancer, right? We also talk about Jesus Christ who's the best in Jesus Christ who's the best in Jesus Christ who's the best in everything. My faith in Jesus Christ everything. My faith in Jesus Christ everything. My faith in Jesus Christ means more to me than anything. If you means more to me than anything. If you means more to me than anything. If you could continue to give or give for the could continue to give or give for the could continue to give or give for the first time, we would certainly first time, we would certainly first time, we would certainly appreciate it. You can go to appreciate it. You can go to appreciate it. You can go to jesusandcience.org/donate. jesusandcience.org/donate. jesusandcience.org/donate. All US donations are taxdeductible. All US donations are taxdeductible. All US donations are taxdeductible. Thank you so much. These kinds of things Thank you so much. These kinds of things Thank you so much. These kinds of things are found in um small organisms, are found in um small organisms, are found in um small organisms, multisellular organisms that typically multisellular organisms that typically multisellular organisms that typically have at least 10,000 transcripts. 10 have at least 10,000 transcripts. 10 have at least 10,000 transcripts. 10 20,000 transcripts. So you got 10 20,000 20,000 transcripts. So you got 10 20,000 20,000 transcripts. So you got 10 20,000 of these different variants in many in of these different variants in many in of these different variants in many in multiple copies. Uh but all of these are multiple copies. Uh but all of these are multiple copies. Uh but all of these are each one of these are different, right?

each one of these are different, right? each one of these are different, right? And you've got to then And you've got to then And you've got to then have one one microna binding to anywhere have one one microna binding to anywhere have one one microna binding to anywhere from one to several thousand of the from one to several thousand of the from one to several thousand of the correct ones. This is the real world. correct ones. This is the real world. correct ones. This is the real world. This is uh known from bionformatics. We This is uh known from bionformatics. We This is uh known from bionformatics. We know that some micronas like this one know that some micronas like this one know that some micronas like this one here may have to downregulate 50 here may have to downregulate 50 here may have to downregulate 50 specific counterpart messenger RNAs. specific counterpart messenger RNAs. specific counterpart messenger RNAs. others only five and others 2,000. So others only five and others 2,000. So others only five and others 2,000. So the numbers are all over the place, but the numbers are all over the place, but the numbers are all over the place, but it's got to be the right ones that is it's got to be the right ones that is it's got to be the right ones that is statistically a mess. statistically a mess. statistically a mess. Now keep in mind that that supposedly Now keep in mind that that supposedly Now keep in mind that that supposedly precarious bacteria and the early single precarious bacteria and the early single precarious bacteria and the early single cell icariats had about one and a half cell icariats had about one and a half cell icariats had about one and a half billion years already to optimize gene billion years already to optimize gene billion years already to optimize gene regulation before any micronas would regulation before any micronas would regulation before any micronas would have been invented. So everything was have been invented. So everything was have been invented. So everything was already like highly optimized and and already like highly optimized and and already like highly optimized and and you know fine-tuned. So there is very you know fine-tuned. So there is very you know fine-tuned. So there is very very little uh room here for improvement very little uh room here for improvement very little uh room here for improvement especially if done regulation is not especially if done regulation is not especially if done regulation is not done exactly the right timing.

done exactly the right timing. done exactly the right timing. So that is a pro that is a a known So that is a pro that is a a known So that is a pro that is a a known problem that the number of those that problem that the number of those that problem that the number of those that would be incorrectly and deleteriously would be incorrectly and deleteriously would be incorrectly and deleteriously downregulated downregulated downregulated the genes would have uh outweighed by the genes would have uh outweighed by the genes would have uh outweighed by sheer chance originally those sheer chance originally those sheer chance originally those beneficially uh downregulated every beneficially uh downregulated every beneficially uh downregulated every generation. generation. generation. Every generation. Every generation. Every generation. So it makes it very very difficult to So it makes it very very difficult to So it makes it very very difficult to explain how could this system have ever explain how could this system have ever explain how could this system have ever started when you got four massive started when you got four massive started when you got four massive disadvant disadvantages. disadvant disadvantages. disadvant disadvantages. So um everything would be uh messed up So um everything would be uh messed up So um everything would be uh messed up and you'd be tying up all kinds of and you'd be tying up all kinds of and you'd be tying up all kinds of resources for no particular benefit. resources for no particular benefit. resources for no particular benefit. I've talked about only and I won't talk I've talked about only and I won't talk I've talked about only and I won't talk about anything else today than the about anything else today than the about anything else today than the problem of getting the the whole process problem of getting the the whole process problem of getting the the whole process started. But of course there is a second started. But of course there is a second started. But of course there is a second problem is how then do all the problem is how then do all the problem is how then do all the additional micronas develop later on? additional micronas develop later on? additional micronas develop later on? Humans have more than 2,000 micronas. So Humans have more than 2,000 micronas. So Humans have more than 2,000 micronas. So 2,000 of these things each one of them 2,000 of these things each one of them 2,000 of these things each one of them has got to be linked to precisely the has got to be linked to precisely the has got to be linked to precisely the right subset right subset right subset of counterpart messenger RNAs at the of counterpart messenger RNAs at the of counterpart messenger RNAs at the right time when they should be right time when they should be right time when they should be downregulated.

Now Now this is um a a a big problem. Um we'll this is um a a a big problem. Um we'll this is um a a a big problem. Um we'll talk a little bit about well-known talk a little bit about well-known talk a little bit about well-known population genetics used by population genetics used by population genetics used by evolutionists. Remember that the evolutionists. Remember that the evolutionists. Remember that the microRNA is initially absent in microRNA is initially absent in microRNA is initially absent in procariats but it is known it's been procariats but it is known it's been procariats but it is known it's been done by a done by a done by a published by a guy called Bartell that published by a guy called Bartell that published by a guy called Bartell that humans flies and worm do have seven humans flies and worm do have seven humans flies and worm do have seven micrnas that are identical. Like I said micrnas that are identical. Like I said micrnas that are identical. Like I said humans of course have got a couple humans of course have got a couple humans of course have got a couple thousand more that are not shared and thousand more that are not shared and thousand more that are not shared and these have many that aren't shared what these have many that aren't shared what these have many that aren't shared what not but they do have 27 in common. not but they do have 27 in common. not but they do have 27 in common. Therefore, the thinking is uhhuh they Therefore, the thinking is uhhuh they Therefore, the thinking is uhhuh they all must have uh inherited them from a all must have uh inherited them from a all must have uh inherited them from a common ancestor way back where the when common ancestor way back where the when common ancestor way back where the when the first uh Belitarian the first uh Belitarian the first uh Belitarian um arose. That is the thinking. So 27 um arose. That is the thinking. So 27 um arose. That is the thinking. So 27 must have arisen pretty quickly. Now is must have arisen pretty quickly. Now is must have arisen pretty quickly. Now is that plausible?

that plausible? that plausible? All of this would have had to start All of this would have had to start All of this would have had to start someplace. Bacteria don't have it. no someplace. Bacteria don't have it. no someplace. Bacteria don't have it. no micronas, no risk proteins, all this is micronas, no risk proteins, all this is micronas, no risk proteins, all this is missing, right? And then at some point missing, right? And then at some point missing, right? And then at some point in time, allegedly in time, allegedly in time, allegedly some metazzoans had them. So it had to some metazzoans had them. So it had to some metazzoans had them. So it had to start someplace. So let's say an start someplace. So let's say an start someplace. So let's say an individual started off with some individual started off with some individual started off with some mutations and this is going to develop mutations and this is going to develop mutations and this is going to develop into the microna technology. into the microna technology. into the microna technology. These organisms like us diploid meaning These organisms like us diploid meaning These organisms like us diploid meaning you've got an all from the father and you've got an all from the father and you've got an all from the father and from the mother. So you got two variants from the mother. So you got two variants from the mother. So you got two variants big A little A. So in population big A little A. So in population big A little A. So in population genetics the little A is considered what genetics the little A is considered what genetics the little A is considered what mutated and every now and then the mutated and every now and then the mutated and every now and then the theory is something good happen a good theory is something good happen a good theory is something good happen a good mutation and that good mutation might mutation and that good mutation might mutation and that good mutation might end up fixing throughout the population. end up fixing throughout the population. end up fixing throughout the population. So there's a mathematical formalism. The So there's a mathematical formalism. The So there's a mathematical formalism. The relative fitness um is defined a certain relative fitness um is defined a certain relative fitness um is defined a certain way. If the offspring has way. If the offspring has way. If the offspring has two of the original alliles which two of the original alliles which two of the original alliles which virtually the whole population would virtually the whole population would virtually the whole population would have had originally then of course the have had originally then of course the have had originally then of course the fitness is one like the rest of the fitness is one like the rest of the fitness is one like the rest of the population right let's say though that population right let's say though that population right let's say though that uh the offspring did inherit uh the offspring did inherit uh the offspring did inherit the mutated al so one is mutated the the mutated al so one is mutated the the mutated al so one is mutated the other is not this fitness is defined as other is not this fitness is defined as other is not this fitness is defined as one plus a fraction of the sele activity

one plus a fraction of the sele activity one plus a fraction of the sele activity coefficient S here coefficient S here coefficient S here h is arbitrary is us usually set to 2.5 h is arbitrary is us usually set to 2.5 h is arbitrary is us usually set to 2.5 and um if the organism had uh both of and um if the organism had uh both of and um if the organism had uh both of these for example one from the male one these for example one from the male one these for example one from the male one from the female then they' be the from the female then they' be the from the female then they' be the fitness would be defined as 1 plus s fitness would be defined as 1 plus s fitness would be defined as 1 plus s this is what is done commonly in this is what is done commonly in this is what is done commonly in population genetics population genetics population genetics um um um keep in mind that the probability of a keep in mind that the probability of a keep in mind that the probability of a carrier of the mutated carrier of the mutated carrier of the mutated ll would have a 50% chance of passing it ll would have a 50% chance of passing it ll would have a 50% chance of passing it on by sheer chance, right? Which means on by sheer chance, right? Which means on by sheer chance, right? Which means therefore a 0.25% therefore a 0.25% therefore a 0.25% probability of passing it on to the probability of passing it on to the probability of passing it on to the grandkids what not. So it means there's grandkids what not. So it means there's grandkids what not. So it means there's already a very low probability of already a very low probability of already a very low probability of anything fixing. So in population anything fixing. So in population anything fixing. So in population genetics you look at things over time. genetics you look at things over time. genetics you look at things over time. Initially you've got a population size n Initially you've got a population size n Initially you've got a population size n some population and it's got two some population and it's got two some population and it's got two alliles. So you got two times n alliles.

alliles. So you got two times n alliles. alliles. So you got two times n alliles. So initially it starts off you know very So initially it starts off you know very So initially it starts off you know very very low proportion in the population. very low proportion in the population. very low proportion in the population. And the question is over time can it end And the question is over time can it end And the question is over time can it end up fixing? So the whole population has up fixing? So the whole population has up fixing? So the whole population has only the mutated al only the mutated al only the mutated al in the case of things that micronas is in the case of things that micronas is in the case of things that micronas is important because when you look at all important because when you look at all important because when you look at all humans all mice all flies take many many humans all mice all flies take many many humans all mice all flies take many many individuals we all have the exact same individuals we all have the exact same individuals we all have the exact same macronas we don't see that 20% of one macronas we don't see that 20% of one macronas we don't see that 20% of one microna is evolving and spreading microna is evolving and spreading microna is evolving and spreading through the population and 35% of through the population and 35% of through the population and 35% of another one and 75% of another one. No, another one and 75% of another one. No, another one and 75% of another one. No, it's all or nothing. It's it's all it's all or nothing. It's it's all it's all or nothing. It's it's all there. And there are many many micronese there. And there are many many micronese there. And there are many many micronese that are species specific. They're only that are species specific. They're only that are species specific. They're only found in one organism and not another. found in one organism and not another. found in one organism and not another. So let's just do a little bit of math. So let's just do a little bit of math. So let's just do a little bit of math. Now in this particular case, remember I Now in this particular case, remember I Now in this particular case, remember I said that there are four reasons why an said that there are four reasons why an said that there are four reasons why an organism carrying or starting off this organism carrying or starting off this organism carrying or starting off this adventure to produce micronese would be adventure to produce micronese would be adventure to produce micronese would be at a disadvantage. Longer genomes, at a disadvantage. Longer genomes, at a disadvantage. Longer genomes, misregulation and everything else. Okay.

misregulation and everything else. Okay. misregulation and everything else. Okay. Usually population genetics when you Usually population genetics when you Usually population genetics when you have a number on the order of 10 - 6 10 have a number on the order of 10 - 6 10 have a number on the order of 10 - 6 10 - 7 is treated as pretty much - 7 is treated as pretty much - 7 is treated as pretty much irrelevant. very very small. So that's irrelevant. very very small. So that's irrelevant. very very small. So that's why I picked a number here that is very why I picked a number here that is very why I picked a number here that is very small because it's very hard to argue small because it's very hard to argue small because it's very hard to argue that I'm being too um demanding. 10^ the that I'm being too um demanding. 10^ the that I'm being too um demanding. 10^ the minus 7 would mean it is slightly minus 7 would mean it is slightly minus 7 would mean it is slightly delterious here. S is negative delterious here. S is negative delterious here. S is negative because we have pointed out four good because we have pointed out four good because we have pointed out four good reasons and nobody disputes this why reasons and nobody disputes this why reasons and nobody disputes this why that would be a problem. Pick a that would be a problem. Pick a that would be a problem. Pick a population size. I say a billion for a population size. I say a billion for a population size. I say a billion for a small population. Note here underneath small population. Note here underneath small population. Note here underneath that that that the neatodes here is ancestor down here the neatodes here is ancestor down here the neatodes here is ancestor down here the common ancestor would have been the common ancestor would have been the common ancestor would have been probably pretty similar. The number is probably pretty similar. The number is probably pretty similar. The number is about 10 to the 20 members. So the about 10 to the 20 members. So the about 10 to the 20 members. So the larger the population the more unlikely larger the population the more unlikely larger the population the more unlikely it's going to ever fix. Right? So this it's going to ever fix. Right? So this it's going to ever fix. Right? So this is actually pretty generous. Bottom line is actually pretty generous. Bottom line is actually pretty generous. Bottom line here is standard population here is standard population here is standard population calculations from any of your college calculations from any of your college calculations from any of your college books will show this equation here.

books will show this equation here. books will show this equation here. Plunk in the numbers S and N and you end Plunk in the numbers S and N and you end Plunk in the numbers S and N and you end up with a a probability that is up with a a probability that is up with a a probability that is ridiculously low 10us 180th that that ridiculously low 10us 180th that that ridiculously low 10us 180th that that would have fixed. You see it's very very would have fixed. You see it's very very would have fixed. You see it's very very very difficult to argue that a very difficult to argue that a very difficult to argue that a dilitterious dilitterious dilitterious mutation is ever going to fix in a mutation is ever going to fix in a mutation is ever going to fix in a relative large population. relative large population. relative large population. That's the bottom line. Okay, that is That's the bottom line. Okay, that is That's the bottom line. Okay, that is the main part. Um, I'm just going to the main part. Um, I'm just going to the main part. Um, I'm just going to finish things off by saying I've talked finish things off by saying I've talked finish things off by saying I've talked about one code, the microna. The about one code, the microna. The about one code, the microna. The hundreds, thousands of other ones we hundreds, thousands of other ones we hundreds, thousands of other ones we won't talk about, but we can do the same won't talk about, but we can do the same won't talk about, but we can do the same kind of analysis. So many of these codes kind of analysis. So many of these codes kind of analysis. So many of these codes have a recognition element placed on the have a recognition element placed on the have a recognition element placed on the DNA. And many of them for example were DNA. And many of them for example were DNA. And many of them for example were to initiate replication of DNA and to initiate replication of DNA and to initiate replication of DNA and bacteria. they have these DNA uh boxes, bacteria. they have these DNA uh boxes, bacteria. they have these DNA uh boxes, several of them, and you know there may several of them, and you know there may several of them, and you know there may many other codes where the recognition many other codes where the recognition many other codes where the recognition element is placed on DNA. We've got a element is placed on DNA. We've got a element is placed on DNA. We've got a bunch of them on RNA. For example, those bunch of them on RNA. For example, those bunch of them on RNA. For example, those of you know about splicing intron of you know about splicing intron of you know about splicing intron exxons.

exxons. exxons. There is a recognition element at the There is a recognition element at the There is a recognition element at the five prime end of the intron, the threep five prime end of the intron, the threep five prime end of the intron, the threep prime end. And you've got tens of prime end. And you've got tens of prime end. And you've got tens of thousands of these, they have to be thousands of these, they have to be thousands of these, they have to be replaced correctly so you don't um end replaced correctly so you don't um end replaced correctly so you don't um end up with all kinds of messed up proteins up with all kinds of messed up proteins up with all kinds of messed up proteins and whatnot. You know, and whatnot. You know, and whatnot. You know, um there many many other examples. Last um there many many other examples. Last um there many many other examples. Last slide, slide, slide, many of these recognition elements are many of these recognition elements are many of these recognition elements are placed on proteins placed on proteins placed on proteins and therefore other proteins can and therefore other proteins can and therefore other proteins can interact there. Um for example, proteins interact there. Um for example, proteins interact there. Um for example, proteins often have to be located to specific often have to be located to specific often have to be located to specific places within the cell. So the code um places within the cell. So the code um places within the cell. So the code um specifies where to go. The code specifies where to go. The code specifies where to go. The code interacts in a manner so that the two interacts in a manner so that the two interacts in a manner so that the two elements recognition element SP uh are elements recognition element SP uh are elements recognition element SP uh are bound together complex formed and this bound together complex formed and this bound together complex formed and this then sends it to either places within then sends it to either places within then sends it to either places within the cell or outside of the cell.

the cell or outside of the cell. the cell or outside of the cell. um you got the end rule code that um you got the end rule code that um you got the end rule code that specifies how long the halflife of a specifies how long the halflife of a specifies how long the halflife of a protein should be etc etc. So we got protein should be etc etc. So we got protein should be etc etc. So we got thousands of codes and all them share thousands of codes and all them share thousands of codes and all them share the same problems mathematical problem the same problems mathematical problem the same problems mathematical problem they've got to start somewhere and to they've got to start somewhere and to they've got to start somewhere and to start you've got to have larger genomes start you've got to have larger genomes start you've got to have larger genomes you with all the disadvantages that I you with all the disadvantages that I you with all the disadvantages that I just mentioned. Okay. Um, some just mentioned. Okay. Um, some just mentioned. Okay. Um, some references and, uh, that's it for now. references and, uh, that's it for now. references and, uh, that's it for now. >> Yeah. I love the, um, the distinction >> Yeah. I love the, um, the distinction >> Yeah. I love the, um, the distinction between digital and analog. You know, between digital and analog. You know, between digital and analog. You know, what we do when we build computers is we what we do when we build computers is we what we do when we build computers is we take the world, right, the analog world take the world, right, the analog world take the world, right, the analog world that we exist in, and we pin it down to that we exist in, and we pin it down to that we exist in, and we pin it down to make it as digital and deterministic as make it as digital and deterministic as make it as digital and deterministic as possible. So, like the silicon in my possible. So, like the silicon in my possible. So, like the silicon in my phone, right? Like that's this etched phone, right? Like that's this etched phone, right? Like that's this etched thing that's static and in place and has thing that's static and in place and has thing that's static and in place and has been tuned. so that you can run ones and been tuned. so that you can run ones and been tuned. so that you can run ones and zeros through it and always get the same zeros through it and always get the same zeros through it and always get the same answer out, right? And and we need to do answer out, right? And and we need to do answer out, right? And and we need to do that because we need to be able to that because we need to be able to that because we need to be able to reason about these things in those terms reason about these things in those terms reason about these things in those terms deterministically. But when you do that, deterministically. But when you do that, deterministically. But when you do that, right, you you take a lot of the right, you you take a lot of the right, you you take a lot of the opportunities that physics presents you opportunities that physics presents you opportunities that physics presents you out of the picture. And what life seems out of the picture. And what life seems out of the picture. And what life seems to do is this really profound blending to do is this really profound blending to do is this really profound blending of digital and analog that to me seems of digital and analog that to me seems of digital and analog that to me seems to leverage all of the available to leverage all of the available to leverage all of the available physics, right? like you get to mix and physics, right? like you get to mix and physics, right? like you get to mix and match all of these different pieces to match all of these different pieces to match all of these different pieces to implement these incredibly complex implement these incredibly complex implement these incredibly complex things and at the same time it's not

things and at the same time it's not things and at the same time it's not this undifferiated mess right um royal I this undifferiated mess right um royal I this undifferiated mess right um royal I think I think some of the language that think I think some of the language that think I think some of the language that I would use as you were describing it I would use as you were describing it I would use as you were describing it you know when you think of the mirna the you know when you think of the mirna the you know when you think of the mirna the microRNAs microRNAs microRNAs that are over here right they need to that are over here right they need to that are over here right they need to regulate the messenger RNAs over here regulate the messenger RNAs over here regulate the messenger RNAs over here and they need to do it contextually so and they need to do it contextually so and they need to do it contextually so there's a lot of like you know logic there's a lot of like you know logic there's a lot of like you know logic embedded in that contextual embedded in that contextual embedded in that contextual um uh uh uh relationship, right? That um uh uh uh relationship, right? That um uh uh uh relationship, right? That contextual regulation. When you step contextual regulation. When you step contextual regulation. When you step back, what what are we doing here? We're back, what what are we doing here? We're back, what what are we doing here? We're talking about two different talking about two different talking about two different abstractions, right? One is a is an abstractions, right? One is a is an abstractions, right? One is a is an abstraction that has to plug into the abstraction that has to plug into the abstraction that has to plug into the other. And the bridge is this sort of other. And the bridge is this sort of other. And the bridge is this sort of digital alignment code, but there's also digital alignment code, but there's also digital alignment code, but there's also analog elements where I could the amount analog elements where I could the amount analog elements where I could the amount of microRNA, the amount of messenger RNA of microRNA, the amount of messenger RNA of microRNA, the amount of messenger RNA is a free floating variable that is a free floating variable that is a free floating variable that controls the function in the cell. But I controls the function in the cell. But I controls the function in the cell. But I think your key point is in order to think your key point is in order to think your key point is in order to enable that abstraction to operate, you enable that abstraction to operate, you enable that abstraction to operate, you need um infrastructure. You need all of need um infrastructure. You need all of need um infrastructure. You need all of this complex protein machinery to make this complex protein machinery to make this complex protein machinery to make that bridge happen. And that's what we that bridge happen. And that's what we that bridge happen. And that's what we see when we look at life. It's these see when we look at life. It's these see when we look at life. It's these abstractions that are interacting and abstractions that are interacting and abstractions that are interacting and they are incredibly complex, but they're they are incredibly complex, but they're they are incredibly complex, but they're complex for good reasons, right? It's complex for good reasons, right? It's complex for good reasons, right? It's just because life is so complex, you just because life is so complex, you just because life is so complex, you need to do that and they're only enabled need to do that and they're only enabled need to do that and they're only enabled by this infrastructure. And so I think by this infrastructure. And so I think by this infrastructure. And so I think your your points about the population your your points about the population your your points about the population genetics, right? like there is a minimum genetics, right? like there is a minimum genetics, right? like there is a minimum amount of infrastructure you need for amount of infrastructure you need for amount of infrastructure you need for any of this stuff to make sense. And I any of this stuff to make sense. And I any of this stuff to make sense. And I think you've done a good job like laying think you've done a good job like laying think you've done a good job like laying out here's all the challenges. How is it out here's all the challenges. How is it out here's all the challenges. How is it all going to come together? It's it's all going to come together? It's it's all going to come together? It's it's tricky. It it really is is quite the

tricky. It it really is is quite the tricky. It it really is is quite the challenge. But uh yeah, I I I went on challenge. But uh yeah, I I I went on challenge. But uh yeah, I I I went on and on, Jen, but I think you know, as a and on, Jen, but I think you know, as a and on, Jen, but I think you know, as a computer guy, I look at this and I go, computer guy, I look at this and I go, computer guy, I look at this and I go, "Yes, and right there's there's "Yes, and right there's there's "Yes, and right there's there's computers hiding in here, but this is so computers hiding in here, but this is so computers hiding in here, but this is so much more. But it's also so much more much more. But it's also so much more much more. But it's also so much more for good reason." Does that make sense? for good reason." Does that make sense? for good reason." Does that make sense? >> Yeah. Thanks, Anie. I really appreciate >> Yeah. Thanks, Anie. I really appreciate >> Yeah. Thanks, Anie. I really appreciate the way you summarized that. Um, it it the way you summarized that. Um, it it the way you summarized that. Um, it it kind of goes to what I was saying that kind of goes to what I was saying that kind of goes to what I was saying that you can use the analogy of a computer you can use the analogy of a computer you can use the analogy of a computer and digital code, but it is so much more and digital code, but it is so much more and digital code, but it is so much more than that. There's there's other aspects than that. There's there's other aspects than that. There's there's other aspects of it that a computer today can't make of it that a computer today can't make of it that a computer today can't make any sense of. Um I was I was thinking as any sense of. Um I was I was thinking as any sense of. Um I was I was thinking as I look at this in the next slide about I look at this in the next slide about I look at this in the next slide about playing devil's advocate a little bit playing devil's advocate a little bit playing devil's advocate a little bit because I know that those who oppose us because I know that those who oppose us because I know that those who oppose us would jump right in and say but would jump right in and say but would jump right in and say but proariots have they also have like small proariots have they also have like small proariots have they also have like small RNAs that come in and inhibit messenger RNAs that come in and inhibit messenger RNAs that come in and inhibit messenger RNA and so they can easily envision an RNA and so they can easily envision an RNA and so they can easily envision an evolutionary process where these small evolutionary process where these small evolutionary process where these small RNAs of proaryots would evolve RNAs of proaryots would evolve RNAs of proaryots would evolve additional features, pull in some additional features, pull in some additional features, pull in some proteins, you know, the the kind of proteins, you know, the the kind of proteins, you know, the the kind of modification process that you have shown modification process that you have shown modification process that you have shown here in a flowchart and that all that here in a flowchart and that all that here in a flowchart and that all that all evolved from a simpler being. So, all evolved from a simpler being. So, all evolved from a simpler being. So, what's your what's your thinking there, what's your what's your thinking there, what's your what's your thinking there, Royal?

Royal? Royal? >> Uh, first um these uh silencing RNA, >> Uh, first um these uh silencing RNA, >> Uh, first um these uh silencing RNA, these small RNAs, there there are such these small RNAs, there there are such these small RNAs, there there are such things there there many of them they things there there many of them they things there there many of them they serve a purpose. They serve they work in serve a purpose. They serve they work in serve a purpose. They serve they work in a different way with a different logic. a different way with a different logic. a different way with a different logic. uh for example one are called peeweee uh for example one are called peeweee uh for example one are called peeweee they they work only uh I think only in they they work only uh I think only in they they work only uh I think only in the germ line to do certain things the the germ line to do certain things the the germ line to do certain things the um microarna are about 22 nucleides long um microarna are about 22 nucleides long um microarna are about 22 nucleides long right right right and of that seven or eight only are used and of that seven or eight only are used and of that seven or eight only are used for the binding purpose why because the for the binding purpose why because the for the binding purpose why because the other part is necessary to interact with other part is necessary to interact with other part is necessary to interact with the proteins and this you don't have the proteins and this you don't have the proteins and this you don't have with the other um type of silencing with the other um type of silencing with the other um type of silencing activities that are far less activities that are far less activities that are far less differentiated, far less regular, far differentiated, far less regular, far differentiated, far less regular, far less precise. less precise. less precise. What we have here I believe is a What we have here I believe is a What we have here I believe is a different um technology entirely. That different um technology entirely. That different um technology entirely. That means the microRNAs are regulated at a means the microRNAs are regulated at a means the microRNAs are regulated at a specific time very very carefully to specific time very very carefully to specific time very very carefully to very very rapidly attached to a certain very very rapidly attached to a certain very very rapidly attached to a certain ensemble of messenger RNAs and that has ensemble of messenger RNAs and that has ensemble of messenger RNAs and that has a very complex outcome. In some case it a very complex outcome. In some case it a very complex outcome. In some case it leads to less protein. In other cases it leads to less protein. In other cases it leads to less protein. In other cases it actually ends up activating. When you actually ends up activating. When you actually ends up activating. When you deactivate a silencing gene it activates deactivate a silencing gene it activates deactivate a silencing gene it activates another gene. is a very very very another gene. is a very very very another gene. is a very very very complex regulatory thing that actually

complex regulatory thing that actually complex regulatory thing that actually you don't see in the bacteria and other you don't see in the bacteria and other you don't see in the bacteria and other kinds of silencing RNAs. It's a kinds of silencing RNAs. It's a kinds of silencing RNAs. It's a different technology. different technology. different technology. >> I I I think it it's absolutely true that >> I I I think it it's absolutely true that >> I I I think it it's absolutely true that there are pieces of this picture like there are pieces of this picture like there are pieces of this picture like there are proteins in the risk complex there are proteins in the risk complex there are proteins in the risk complex that associate with those I believe this that associate with those I believe this that associate with those I believe this is true small RNAs in the proarotic is true small RNAs in the proarotic is true small RNAs in the proarotic context. And so I think what you see context. And so I think what you see context. And so I think what you see when you dig into literature on this is when you dig into literature on this is when you dig into literature on this is there's this sort of qualitative story there's this sort of qualitative story there's this sort of qualitative story of co-option and neutral drift and of co-option and neutral drift and of co-option and neutral drift and things coming together that on the face things coming together that on the face things coming together that on the face of it makes for a decent sounding story. of it makes for a decent sounding story. of it makes for a decent sounding story. I think the challenge is you know I think the challenge is you know I think the challenge is you know validating that that story works and and validating that that story works and and validating that that story works and and there again we bump into some math right there again we bump into some math right there again we bump into some math right some waiting time math. And just to just some waiting time math. And just to just some waiting time math. And just to just to point out one one simple thing, Roy, to point out one one simple thing, Roy, to point out one one simple thing, Roy, if you go to your next slide where you if you go to your next slide where you if you go to your next slide where you do the population genetics, we don't do the population genetics, we don't do the population genetics, we don't have to dig into any of the math at all have to dig into any of the math at all have to dig into any of the math at all really, but there's just a fundamental really, but there's just a fundamental really, but there's just a fundamental thing which is if you want a neutral or thing which is if you want a neutral or thing which is if you want a neutral or slightly delotterious mutation to fix, slightly delotterious mutation to fix, slightly delotterious mutation to fix, you need a smaller population size.

you need a smaller population size. you need a smaller population size. That's generally what you need. That's That's generally what you need. That's That's generally what you need. That's that's the result of population that's the result of population that's the result of population genetics. But the problem with the genetics. But the problem with the genetics. But the problem with the smaller population size is that's less smaller population size is that's less smaller population size is that's less uh uh search area for a random search, uh uh search area for a random search, uh uh search area for a random search, right? you can't do as much random right? you can't do as much random right? you can't do as much random search and so um you're stuck with not search and so um you're stuck with not search and so um you're stuck with not being able to search very effectively if being able to search very effectively if being able to search very effectively if the population size is small. To be able the population size is small. To be able the population size is small. To be able to search effectively, you need a larger to search effectively, you need a larger to search effectively, you need a larger population size. But a larger population population size. But a larger population population size. But a larger population size is precisely where it's very hard size is precisely where it's very hard size is precisely where it's very hard to fix these sorts of things. And so the to fix these sorts of things. And so the to fix these sorts of things. And so the bridge to get from these highly bridge to get from these highly bridge to get from these highly optimized billions of them procariots optimized billions of them procariots optimized billions of them procariots that Royal is talking about to this new that Royal is talking about to this new that Royal is talking about to this new mechanism mechanism mechanism that's an interesting quantitative that's an interesting quantitative that's an interesting quantitative question that that just so story which question that that just so story which question that that just so story which has some merit to it would need to has some merit to it would need to has some merit to it would need to thread and so let's you know like thread and so let's you know like thread and so let's you know like digging into that would be the next step digging into that would be the next step digging into that would be the next step to really validate whether that story is to really validate whether that story is to really validate whether that story is true. And the way they might thread that true. And the way they might thread that true. And the way they might thread that is to say that you have a large is to say that you have a large is to say that you have a large population that developed this population that developed this population that developed this beneficial mutation and then you had a beneficial mutation and then you had a beneficial mutation and then you had a bottleneck where most of them died off bottleneck where most of them died off bottleneck where most of them died off but the the one that had it you know but the the one that had it you know but the the one that had it you know carried forward and and then it fixed.

carried forward and and then it fixed. carried forward and and then it fixed. Yeah. Yeah. Yeah. >> So all of these statements right are >> So all of these statements right are >> So all of these statements right are things that one would have to like things that one would have to like things that one would have to like really dig into and understand. And I really dig into and understand. And I really dig into and understand. And I think what's striking for me is that think what's striking for me is that think what's striking for me is that what comes out at the other end and this what comes out at the other end and this what comes out at the other end and this to just bring it back to Royal what you to just bring it back to Royal what you to just bring it back to Royal what you talked about how we humans seem to be talked about how we humans seem to be talked about how we humans seem to be uniquely poised to see codes and uniquely poised to see codes and uniquely poised to see codes and languages, right? What comes out the languages, right? What comes out the languages, right? What comes out the other end is actually this abstraction other end is actually this abstraction other end is actually this abstraction that works that's relatively clean and that works that's relatively clean and that works that's relatively clean and can be reasoned about and um and really can be reasoned about and um and really can be reasoned about and um and really does look like uh uh an analog digital does look like uh uh an analog digital does look like uh uh an analog digital hybrid code for managing regulatory hybrid code for managing regulatory hybrid code for managing regulatory needs not just for single cells but by needs not just for single cells but by needs not just for single cells but by this point multisellular organisms. This this point multisellular organisms. This this point multisellular organisms. This is the key stuff that controls what is the key stuff that controls what is the key stuff that controls what different cells in our bodies are doing different cells in our bodies are doing different cells in our bodies are doing in different contexts through time and in different contexts through time and in different contexts through time and space and environment. So space and environment. So space and environment. So >> um back to Rob's comment, it is true >> um back to Rob's comment, it is true >> um back to Rob's comment, it is true that one can envision all kinds of that one can envision all kinds of that one can envision all kinds of scenarios and the ones should you know scenarios and the ones should you know scenarios and the ones should you know look into it deeply and seriously look into it deeply and seriously look into it deeply and seriously mathematically. mathematically. mathematically. Um and that is where I I think I want to Um and that is where I I think I want to Um and that is where I I think I want to pose a challenge because um you're pose a challenge because um you're pose a challenge because um you're right. One could argue that in a large right. One could argue that in a large right. One could argue that in a large population things got started and then population things got started and then population things got started and then founder groups uh wandered off and got founder groups uh wandered off and got founder groups uh wandered off and got lucky and and then it got fixed and then lucky and and then it got fixed and then lucky and and then it got fixed and then they overcame the big population again.

they overcame the big population again. they overcame the big population again. Um one can up can come up with these Um one can up can come up with these Um one can up can come up with these kinds of stories. The trouble is you kinds of stories. The trouble is you kinds of stories. The trouble is you lose all credibility when you actually lose all credibility when you actually lose all credibility when you actually start to look into that seriously. For start to look into that seriously. For start to look into that seriously. For example, uh humans have uh on the order example, uh humans have uh on the order example, uh humans have uh on the order of 700 micronas missing in rats. Okay, of 700 micronas missing in rats. Okay, of 700 micronas missing in rats. Okay, in m sorry in mice. Now, we're supposed in m sorry in mice. Now, we're supposed in m sorry in mice. Now, we're supposed to have a common ancestor 80 million to have a common ancestor 80 million to have a common ancestor 80 million years ago. H how many times are you years ago. H how many times are you years ago. H how many times are you going to argue that stuff like that going to argue that stuff like that going to argue that stuff like that actually happened? and all that uh actually happened? and all that uh actually happened? and all that uh because they they are completely fixed because they they are completely fixed because they they are completely fixed in the entire mice population. The ones in the entire mice population. The ones in the entire mice population. The ones they have and the ones we have are they have and the ones we have are they have and the ones we have are completely fixed and we see no evidence completely fixed and we see no evidence completely fixed and we see no evidence of any kind of a of a let's say of any kind of a of a let's say of any kind of a of a let's say spreading into the population phase. spreading into the population phase. spreading into the population phase. So one can argue like that once or twice So one can argue like that once or twice So one can argue like that once or twice when you start talking about all of the when you start talking about all of the when you start talking about all of the numerous micronas that that are distinct numerous micronas that that are distinct numerous micronas that that are distinct to to a particular species and not to to to a particular species and not to to to a particular species and not to another one.

another one. another one. Um Um Um you could say all of evolutionary time you could say all of evolutionary time you could say all of evolutionary time and and organisms were dedicated to and and organisms were dedicated to and and organisms were dedicated to doing that. What do you do with the doing that. What do you do with the doing that. What do you do with the other hundreds of of codes that now other hundreds of of codes that now other hundreds of of codes that now would have had no selective advantage? would have had no selective advantage? would have had no selective advantage? You can't have evolution doing You can't have evolution doing You can't have evolution doing everything at the same time. You only everything at the same time. You only everything at the same time. You only have so many resources, have so many resources, have so many resources, so much time, so many individuals, so so much time, so many individuals, so so much time, so many individuals, so many attempts possible. And that's where many attempts possible. And that's where many attempts possible. And that's where I think the serious quantitative work I think the serious quantitative work I think the serious quantitative work has to be done. And the models I've has to be done. And the models I've has to be done. And the models I've looked into show is is hopeless. looked into show is is hopeless. looked into show is is hopeless. You know, it's it's to to think of a You know, it's it's to to think of a You know, it's it's to to think of a just a cellular structure having all of just a cellular structure having all of just a cellular structure having all of this. At the beginning, you had showed this. At the beginning, you had showed this. At the beginning, you had showed what looked like a cell full of what looked like a cell full of what looked like a cell full of mechanical devices, which is which is mechanical devices, which is which is mechanical devices, which is which is again an analogy so that people could again an analogy so that people could again an analogy so that people could realize how complex this system is. when realize how complex this system is. when realize how complex this system is. when you have a a buildup of CO2 and it needs you have a a buildup of CO2 and it needs you have a a buildup of CO2 and it needs to get more of that CO2 out of the to get more of that CO2 out of the to get more of that CO2 out of the system. uh just all the sensors that are system. uh just all the sensors that are system. uh just all the sensors that are sensing that and then that is that is uh sensing that and then that is that is uh sensing that and then that is that is uh um causing causing more DNA to be read um causing causing more DNA to be read um causing causing more DNA to be read and then you get this transcription and and then you get this transcription and and then you get this transcription and all of the things that have to take all of the things that have to take all of the things that have to take place just because of a a little bit too place just because of a a little bit too place just because of a a little bit too much CO2 building up and and so it has much CO2 building up and and so it has much CO2 building up and and so it has to it has to remove that CO2. It has to

to it has to remove that CO2. It has to to it has to remove that CO2. It has to move protons around in order to to to move protons around in order to to to move protons around in order to to to deal with that. And there's so much that deal with that. And there's so much that deal with that. And there's so much that is tied together in this. And I I I feel is tied together in this. And I I I feel is tied together in this. And I I I feel that, you know, we we we just talk about that, you know, we we we just talk about that, you know, we we we just talk about a cellular structure and we we look at a cellular structure and we we look at a cellular structure and we we look at it in such a simple way, even even as it in such a simple way, even even as it in such a simple way, even even as simple as a as a computer program, but simple as a as a computer program, but simple as a as a computer program, but there's so many different uh pieces there's so many different uh pieces there's so many different uh pieces coming into this. And then if you talk coming into this. And then if you talk coming into this. And then if you talk about a bacterium where it has it has about a bacterium where it has it has about a bacterium where it has it has information that's coming not from information that's coming not from information that's coming not from within itself. It's information coming within itself. It's information coming within itself. It's information coming from the outside. So as there's a from the outside. So as there's a from the outside. So as there's a microtubule that'll drop something off microtubule that'll drop something off microtubule that'll drop something off and then the surface of our cells have and then the surface of our cells have and then the surface of our cells have all of these these sugar these all of these these sugar these all of these these sugar these polysaccharides displayed. And these polysaccharides displayed. And these polysaccharides displayed. And these polysaccharides polysaccharides polysaccharides end up interfacing with other moyateses end up interfacing with other moyateses end up interfacing with other moyateses and and that's how the cells recognize and and that's how the cells recognize and and that's how the cells recognize that hey there's there's another one of that hey there's there's another one of that hey there's there's another one of my my similar cell types bumping up my my similar cell types bumping up my my similar cell types bumping up against me. And all of a sudden that against me. And all of a sudden that against me. And all of a sudden that triggers a whole stream of down triggers a whole stream of down triggers a whole stream of down downstream events that is going to have downstream events that is going to have downstream events that is going to have to lead to to more information being to lead to to more information being to lead to to more information being read off of DNA to deal with that read off of DNA to deal with that read off of DNA to deal with that situation in a positive or a negative situation in a positive or a negative situation in a positive or a negative way, but to deal with it. And there's so way, but to deal with it. And there's so way, but to deal with it. And there's so many uh different areas of inputs coming many uh different areas of inputs coming many uh different areas of inputs coming into this. You know, when you when you into this. You know, when you when you into this. You know, when you when you open up a lot of different programs on

open up a lot of different programs on open up a lot of different programs on your computer, a lot of times it just your computer, a lot of times it just your computer, a lot of times it just hangs up and it just uh it just locks hangs up and it just uh it just locks hangs up and it just uh it just locks and and you got to restart or or you got and and you got to restart or or you got and and you got to restart or or you got to start, you know, you got to hit uh to start, you know, you got to hit uh to start, you know, you got to hit uh control altdelete and and then start control altdelete and and then start control altdelete and and then start closing some programs in order to deal closing some programs in order to deal closing some programs in order to deal with this. And in the old days, you had with this. And in the old days, you had with this. And in the old days, you had to totally reboot. But you think of all to totally reboot. But you think of all to totally reboot. But you think of all the different sensor inputs. You you the different sensor inputs. You you the different sensor inputs. You you just bump up against a cell and it it it just bump up against a cell and it it it just bump up against a cell and it it it it's its lipid billayer knows that it's its lipid billayer knows that it's its lipid billayer knows that pertibbation and that has set off a pertibbation and that has set off a pertibbation and that has set off a chain of more information that has to be chain of more information that has to be chain of more information that has to be transferred and then ultimately more transferred and then ultimately more transferred and then ultimately more proteins being synthesized to to deal proteins being synthesized to to deal proteins being synthesized to to deal with this very situation. with this very situation. with this very situation. Uh so so it's such a complex system Uh so so it's such a complex system Uh so so it's such a complex system within a cell. this idea that you could within a cell. this idea that you could within a cell. this idea that you could you could just take four different four you could just take four different four you could just take four different four different molecular structure types and different molecular structure types and different molecular structure types and mix them together and get get a working mix them together and get get a working mix them together and get get a working cell is is so far from the truth and cell is is so far from the truth and cell is is so far from the truth and every time I see a presentation like every time I see a presentation like every time I see a presentation like this if all the the layers of details this if all the the layers of details this if all the the layers of details that are here it it makes me makes me that are here it it makes me makes me that are here it it makes me makes me get uh frustrated with the just so get uh frustrated with the just so get uh frustrated with the just so stories that are put out there to stories that are put out there to stories that are put out there to minimize the these these sorts of uh minimize the these these sorts of uh minimize the these these sorts of uh structures structures structures >> and it's all too easy to make up a just >> and it's all too easy to make up a just >> and it's all too easy to make up a just so story and uh and then teach it in so story and uh and then teach it in so story and uh and then teach it in class and all the students are accepting class and all the students are accepting class and all the students are accepting accepting it and they just move on and accepting it and they just move on and accepting it and they just move on and >> but the reality is always much more >> but the reality is always much more >> but the reality is always much more complicated complicated complicated >> and this is what you hear on the

>> and this is what you hear on the >> and this is what you hear on the internet these biologists will come on internet these biologists will come on internet these biologists will come on and they'll say things and they'll say a and they'll say things and they'll say a and they'll say things and they'll say a just so story with no appreciation for just so story with no appreciation for just so story with no appreciation for what happens with their just so story what happens with their just so story what happens with their just so story what would have to happen uh uh with what would have to happen uh uh with what would have to happen uh uh with with these sorts of things going on and with these sorts of things going on and with these sorts of things going on and and you try to engage these these and you try to engage these these and you try to engage these these biologists and they won't engage because biologists and they won't engage because biologists and they won't engage because they like their gesso stories. They they like their gesso stories. They they like their gesso stories. They don't want to try to think of the don't want to try to think of the don't want to try to think of the downstream details of those things going downstream details of those things going downstream details of those things going on. And what we're going to see in the on. And what we're going to see in the on. And what we're going to see in the coming weeks and months is that we're coming weeks and months is that we're coming weeks and months is that we're going to be inviting uh uh biologists to going to be inviting uh uh biologists to going to be inviting uh uh biologists to come and to talk with us to and and come and to talk with us to and and come and to talk with us to and and origin of life people. We're going to origin of life people. We're going to origin of life people. We're going to invite them and say, "Okay, tell us tell invite them and say, "Okay, tell us tell invite them and say, "Okay, tell us tell us how life started in in your mind. How us how life started in in your mind. How us how life started in in your mind. How did this whole thing come about? Just did this whole thing come about? Just did this whole thing come about? Just bring us through some of these steps." bring us through some of these steps." bring us through some of these steps." And when we don't understand, we we'll And when we don't understand, we we'll And when we don't understand, we we'll just ask you to clarify a little bit. just ask you to clarify a little bit. just ask you to clarify a little bit. And why is it that you're so you're so And why is it that you're so you're so And why is it that you're so you're so comfortable with this? And uh the comfortable with this? And uh the comfortable with this? And uh the problem that we're going to see is that problem that we're going to see is that problem that we're going to see is that none of these folks are going to want to none of these folks are going to want to none of these folks are going to want to come on and talk with us because they come on and talk with us because they come on and talk with us because they realize that as soon as you start poking realize that as soon as you start poking realize that as soon as you start poking at this thing just a little bit, it at this thing just a little bit, it at this thing just a little bit, it withers very quickly around the edges withers very quickly around the edges withers very quickly around the edges for their their their just so stories.

for their their their just so stories. for their their their just so stories. And this is what we're going to see. So And this is what we're going to see. So And this is what we're going to see. So I I I I I know that that that Royal went I I I I I know that that that Royal went I I I I I know that that that Royal went through a lot of details here and uh um through a lot of details here and uh um through a lot of details here and uh um and it's it's hard for people to grasp and it's it's hard for people to grasp and it's it's hard for people to grasp it, but it it's just so much is it, but it it's just so much is it, but it it's just so much is happening within just a single cell and happening within just a single cell and happening within just a single cell and so much would have had to go on. And if so much would have had to go on. And if so much would have had to go on. And if this is going to to take that this is going to to take that this is going to to take that information and and propagate this and information and and propagate this and information and and propagate this and and translate this to offspring over and and translate this to offspring over and and translate this to offspring over and over again and then lead to the over again and then lead to the over again and then lead to the diversity of life, uh um you know, we diversity of life, uh um you know, we diversity of life, uh um you know, we we're going to really love to have we're going to really love to have we're going to really love to have biologists come on, but what the biologists come on, but what the biologists come on, but what the audience is going to see is that these audience is going to see is that these audience is going to see is that these biologists are not going to come on. And biologists are not going to come on. And biologists are not going to come on. And so what I'm going to do is I'm going to so what I'm going to do is I'm going to so what I'm going to do is I'm going to document all the people that we've document all the people that we've document all the people that we've invited on to just tell us their story invited on to just tell us their story invited on to just tell us their story about how this is even to talk about about how this is even to talk about about how this is even to talk about their own published work and how that their own published work and how that their own published work and how that fits into this scenario of of life or fits into this scenario of of life or fits into this scenario of of life or the the the uh uh the propagation of the the the uh uh the propagation of the the the uh uh the propagation of life and and help us to understand this life and and help us to understand this life and and help us to understand this because it it gets it gets really because it it gets it gets really because it it gets it gets really complicated really quickly and and to complicated really quickly and and to complicated really quickly and and to just keep throwing out just those just keep throwing out just those just keep throwing out just those stories. is we're going to be calling stories. is we're going to be calling stories. is we're going to be calling people on it and uh people are going to people on it and uh people are going to people on it and uh people are going to see that that these people that have see that that these people that have see that that these people that have projected themselves as knowing this and projected themselves as knowing this and projected themselves as knowing this and suggesting that everybody knows this, suggesting that everybody knows this, suggesting that everybody knows this, everybody accepts this, this is really everybody accepts this, this is really everybody accepts this, this is really well defined, this is going to start well defined, this is going to start well defined, this is going to start going away and these things are going to going away and these things are going to going away and these things are going to start collapsing because when you see start collapsing because when you see start collapsing because when you see things like this and things that that

things like this and things that that things like this and things that that that Rob and Unie have gone through in that Rob and Unie have gone through in that Rob and Unie have gone through in the last few conversations that we've the last few conversations that we've the last few conversations that we've had, uh these things are utterly amazing had, uh these things are utterly amazing had, uh these things are utterly amazing and the amount of information transfer and the amount of information transfer and the amount of information transfer that's needed is enormous. So those are that's needed is enormous. So those are that's needed is enormous. So those are some of my thoughts. some of my thoughts. some of my thoughts. >> It's kind of ridiculous when you hear >> It's kind of ridiculous when you hear >> It's kind of ridiculous when you hear statements like statements like statements like in a uh periodic world um somehow the in a uh periodic world um somehow the in a uh periodic world um somehow the chemicals sort of got together and chemicals sort of got together and chemicals sort of got together and formed something lifelike and then formed something lifelike and then formed something lifelike and then Darwinian process take took over and Darwinian process take took over and Darwinian process take took over and everything's easy. everything's easy. everything's easy. >> Yeah. What? >> Yeah. What? >> Yeah. What? >> Yeah. Just throw it at the feet of >> Yeah. Just throw it at the feet of >> Yeah. Just throw it at the feet of Darwin Darwin Darwin >> and then every everything is solved. >> and then every everything is solved. >> and then every everything is solved. >> Yeah. Uh uh but but we're going to ask >> Yeah. Uh uh but but we're going to ask >> Yeah. Uh uh but but we're going to ask the questions. We're going to say, the questions. We're going to say, the questions. We're going to say, "Okay, take us through that process. "Okay, take us through that process. "Okay, take us through that process. Explain to us how that happens. Why why Explain to us how that happens. Why why Explain to us how that happens. Why why are you so comfortable with this? are you so comfortable with this? are you so comfortable with this? >> Why do you are so embracing of this? Uh >> Why do you are so embracing of this? Uh >> Why do you are so embracing of this? Uh uh take us through this." uh take us through this." uh take us through this." >> And uh e even even something as simple >> And uh e even even something as simple >> And uh e even even something as simple as a lipid billayer where the outside is as a lipid billayer where the outside is as a lipid billayer where the outside is different than the inside. different than the inside. different than the inside. >> Yeah, >> Yeah, >> Yeah, >> we don't know how to do that. Yeah.

>> we don't know how to do that. Yeah. >> we don't know how to do that. Yeah. >> Uh we don't know how to make those sorts >> Uh we don't know how to make those sorts >> Uh we don't know how to make those sorts of things in our laboratories, but of things in our laboratories, but of things in our laboratories, but you're going to have to have that when you're going to have to have that when you're going to have to have that when you have a a proton gradient and and uh you have a a proton gradient and and uh you have a a proton gradient and and uh very very simple things. Uh and then you very very simple things. Uh and then you very very simple things. Uh and then you start getting into the complexity of of start getting into the complexity of of start getting into the complexity of of passing on traits to offspring and all passing on traits to offspring and all passing on traits to offspring and all the signaling that has to take place for the signaling that has to take place for the signaling that has to take place for a cell to divide. a cell to divide. a cell to divide. >> Uh explain sort of these things. It's >> Uh explain sort of these things. It's >> Uh explain sort of these things. It's it's going to get quite complex, but it's going to get quite complex, but it's going to get quite complex, but that's where we're going to move to. Uh that's where we're going to move to. Uh that's where we're going to move to. Uh uh Rob, I'm going to give you uh your uh Rob, I'm going to give you uh your uh Rob, I'm going to give you uh your final word and then I'll go to Uni and final word and then I'll go to Uni and final word and then I'll go to Uni and and to uh uh uh to Royal and we're going and to uh uh uh to Royal and we're going and to uh uh uh to Royal and we're going to close it out. to close it out. to close it out. >> Yeah, I just want to thank you Royal >> Yeah, I just want to thank you Royal >> Yeah, I just want to thank you Royal because I hadn't studied microRNAs in because I hadn't studied microRNAs in because I hadn't studied microRNAs in any depth before and so this was eye any depth before and so this was eye any depth before and so this was eye opening to me and every every it's kind opening to me and every every it's kind opening to me and every every it's kind of like peeling back an onion. Every of like peeling back an onion. Every of like peeling back an onion. Every layer you peel back you're like wow layer you peel back you're like wow layer you peel back you're like wow that's awesome. All this complexity and that's awesome. All this complexity and that's awesome. All this complexity and it all comes together to make life and it all comes together to make life and it all comes together to make life and that's beautiful and we give glory to that's beautiful and we give glory to that's beautiful and we give glory to God for that. So, thanks for opening our God for that. So, thanks for opening our God for that. So, thanks for opening our eyes for that. eyes for that. eyes for that. >> Indeed. Aren't you? >> Indeed. Aren't you? >> Indeed. Aren't you? >> Yeah. Yeah. Thanks for diving deep into, >> Yeah. Yeah. Thanks for diving deep into, >> Yeah. Yeah. Thanks for diving deep into, you know, this was just one one system you know, this was just one one system you know, this was just one one system and there are many. Uh, so I I and there are many. Uh, so I I and there are many. Uh, so I I appreciate being able to to go a little appreciate being able to to go a little appreciate being able to to go a little bit deeper on on at least one of them bit deeper on on at least one of them bit deeper on on at least one of them and uh we'll have to save the rest for and uh we'll have to save the rest for and uh we'll have to save the rest for later. But yeah, appreciate it.

later. But yeah, appreciate it. later. But yeah, appreciate it. >> Uh uh Royal, do do you have anything you >> Uh uh Royal, do do you have anything you >> Uh uh Royal, do do you have anything you want? I think in all future discussions want? I think in all future discussions want? I think in all future discussions uh what is going to be necessary is to uh what is going to be necessary is to uh what is going to be necessary is to have our biology friends have our biology friends have our biology friends um accept that the level of precision um accept that the level of precision um accept that the level of precision here necessary for things to work is here necessary for things to work is here necessary for things to work is beyond anything that they have thought beyond anything that they have thought beyond anything that they have thought through. I'm showing a screen right here through. I'm showing a screen right here through. I'm showing a screen right here of the risk complex just to illustrate. of the risk complex just to illustrate. of the risk complex just to illustrate. A precise little portion A precise little portion A precise little portion less than 1,000 of the surface of a less than 1,000 of the surface of a less than 1,000 of the surface of a protein has to have just the right protein has to have just the right protein has to have just the right characteristics to interact with a characteristics to interact with a characteristics to interact with a precise other portion of another precise other portion of another precise other portion of another protein. This is the way the things protein. This is the way the things protein. This is the way the things work. The level engineering is work. The level engineering is work. The level engineering is phenomenal. phenomenal. phenomenal. You would have utter chaos if anything You would have utter chaos if anything You would have utter chaos if anything like this were to have evolved. It would like this were to have evolved. It would like this were to have evolved. It would it would never never never work and be it would never never never work and be it would never never never work and be passed on next generation. All the passed on next generation. All the passed on next generation. All the precise details.

precise details. precise details. >> Yes. >> Yes. >> Yes. >> It's just not a protein, a gene, a >> It's just not a protein, a gene, a >> It's just not a protein, a gene, a mutation. mutation. mutation. >> You can't form complexes with 10 >> You can't form complexes with 10 >> You can't form complexes with 10 proteins where the interfaces got to be proteins where the interfaces got to be proteins where the interfaces got to be precisely just so for a function. And precisely just so for a function. And precisely just so for a function. And you got thousands of these complexes. you got thousands of these complexes. you got thousands of these complexes. These are the things that cannot just These are the things that cannot just These are the things that cannot just simply be brushed off. This is the simply be brushed off. This is the simply be brushed off. This is the reality, the engineering reality of a reality, the engineering reality of a reality, the engineering reality of a cell. cell. cell. >> And and anie just sent us a paper, maybe >> And and anie just sent us a paper, maybe >> And and anie just sent us a paper, maybe we'll talk about it at some point, where we'll talk about it at some point, where we'll talk about it at some point, where they're trying to bring these ideas from they're trying to bring these ideas from they're trying to bring these ideas from genetics and and evolution into in into genetics and and evolution into in into genetics and and evolution into in into origin of life. And so we're going to origin of life. And so we're going to origin of life. And so we're going to get we're going to get more gesso get we're going to get more gesso get we're going to get more gesso stories uh coming into this. And and so stories uh coming into this. And and so stories uh coming into this. And and so uh on see somebody else had sent me the uh on see somebody else had sent me the uh on see somebody else had sent me the same paper. same paper. same paper. >> Okay, great. So, so it's it's it's cap >> Okay, great. So, so it's it's it's cap >> Okay, great. So, so it's it's it's cap capturing a lot of people's attention capturing a lot of people's attention capturing a lot of people's attention and so may maybe you'll have to give us and so may maybe you'll have to give us and so may maybe you'll have to give us a talk on that sometime. a talk on that sometime. a talk on that sometime. But uh yeah. Yeah. But but um But uh yeah. Yeah. But but um But uh yeah. Yeah. But but um >> uh uh yeah, these just so stories are >> uh uh yeah, these just so stories are >> uh uh yeah, these just so stories are just killers. And uh um and I I look just killers. And uh um and I I look just killers. And uh um and I I look through these papers and right away through these papers and right away through these papers and right away being being a chemist, I start flipping being being a chemist, I start flipping being being a chemist, I start flipping the pages. I I don't want to see the the pages. I I don't want to see the the pages. I I don't want to see the words. I want to see the chemistry. I words. I want to see the chemistry. I words. I want to see the chemistry. I want to see the chem. and I get to the want to see the chem. and I get to the want to see the chem. and I get to the end, I haven't seen any chemistry, and end, I haven't seen any chemistry, and end, I haven't seen any chemistry, and so so then then I know something's up so so then then I know something's up so so then then I know something's up here, here, here, >> you know? If you can't show me the >> you know? If you can't show me the >> you know? If you can't show me the chemistry, uh it's like, show me the chemistry, uh it's like, show me the chemistry, uh it's like, show me the money. I mean, show me the chemistry. Uh money. I mean, show me the chemistry. Uh money. I mean, show me the chemistry. Uh uh when you when you have to show the uh when you when you have to show the uh when you when you have to show the chemistry, then you have to know

chemistry, then you have to know chemistry, then you have to know something. And uh uh so anyway, we'll something. And uh uh so anyway, we'll something. And uh uh so anyway, we'll we'll we'll flip through those papers, we'll we'll flip through those papers, we'll we'll flip through those papers, see if we can if we can see even even see if we can if we can see even even see if we can if we can see even even just a simple little molecule somewhere just a simple little molecule somewhere just a simple little molecule somewhere in that paper that'll give us an idea. in that paper that'll give us an idea. in that paper that'll give us an idea. Well, thank you guys. And uh um we'll Well, thank you guys. And uh um we'll Well, thank you guys. And uh um we'll try to tackle some more problems. And uh try to tackle some more problems. And uh try to tackle some more problems. And uh uh it it it's um it's really not that uh it it it's um it's really not that uh it it it's um it's really not that hard. I mean, you can you can just close hard. I mean, you can you can just close hard. I mean, you can you can just close your eyes and pick out any paper and and your eyes and pick out any paper and and your eyes and pick out any paper and and and go at it. And and that's what we're and go at it. And and that's what we're and go at it. And and that's what we're doing. We're selecting we're selecting doing. We're selecting we're selecting doing. We're selecting we're selecting certain ones, but it's not really like certain ones, but it's not really like certain ones, but it's not really like we're we're down selecting the easy ones we're we're down selecting the easy ones we're we're down selecting the easy ones to pick on. I mean, they're they're it's to pick on. I mean, they're they're it's to pick on. I mean, they're they're it's it's not that hard. Anyway, God bless it's not that hard. Anyway, God bless it's not that hard. Anyway, God bless you, my friends. The Lord be with you. you, my friends. The Lord be with you. you, my friends. The Lord be with you. And now we've again seen the the And now we've again seen the the And now we've again seen the the splenders of our God. splenders of our God. splenders of our God. >> The chemistry cries out that it could >> The chemistry cries out that it could >> The chemistry cries out that it could not have happened this way. But like not have happened this way. But like not have happened this way. But like it's this very powerful visceral it's this very powerful visceral it's this very powerful visceral question that I get struck by sometimes. question that I get struck by sometimes. question that I get struck by sometimes. >> They try to find the best series of >> They try to find the best series of >> They try to find the best series of conditions to get a super high yield.

conditions to get a super high yield. conditions to get a super high yield. >> A magical device. And the more we learn >> A magical device. And the more we learn >> A magical device. And the more we learn about it, the more marvelous it becomes.